Para investor dan analis pasar saham akan selalu tertarik dalam mencari cara untuk memaksimalkan keuntungan yang didapatkan dari investasi saham. Salah satu teori paling awal yang mencoba menjelaskan apa yang mempengaruhi besarnya keuntungan dari investasi adalah model Capital Asset Pricing Model (CAPM), yang mengukur return investasi dari segi resiko yang dimiliki oleh investasi tersebut, return yang diharapkan, dan juga return dari instrumen investasi bebas resiko. Namun model ini dianggap kurang lengkap karena hanya memfaktorkan resiko external (kondisi makroekonomi, sentimen investor, dan sebagainya) yang dihadapi oleh saham itu sendiri. Ketidakpuasan ini memunculkan teori baru yang disebut Fama-French Model, yang dimana resiko tidak hanya diukur dari kondisi eksternal pasar namun juga faktor resiko internal perusahaan (diwakilkan oleh ukuran perusahaan dan juga nilai buku perusahaan).
Perkembangan teori tersebut tidak berhenti sampai situ. Sekarang sudah ada berbagai teori yang mencoba menjelaskan faktor-faktor resiko apa saja yang berhubungan dengan pergerakan return sebuah investasi. Pada artikel kali ini, penulis tertarik untuk melihat apakah terdapat hubungan korelasi antara pergantian harga saham pada setiap harinya dengan perubahan volume transaksi saham setiap harinya.
Penulis sadar bahwa hasil analisa pada artikel ini tidak dapat menjadi patokan baku mengenai bagaimana cara mendapatkan keuntungan sebesar mungkin, namun penulis tetap berharap bahwa artikel ini dapat menjadi sudut pandang tambahan bagi para pembaca yang tertarik untuk berinvestasi saham. Artikel ini ditulis karena penulis memiliki ketertarikan mengenai kemungkinan adanya hubungan antara saham dan juga jumlah transaksi yang dilakukan pada saham tersebut.
Sebelum menjelaskan teori-toeri yang digunakan, penulis akan memberikan definisi singkat mengenai variabel apa saja yang diperlukan untuk menjalankan analisa. Variabel pertama adalah Daily Return, yang merupakan persentase perbedaan antara harga saham pada hari ini dengan hari sebelumnya, sedangkan Liquidity (atau likuiditas) dapat didefiniskan sebagai kemampuan investor untuk melakukan banyak transaksi dalam waktu yang singkat, tanpa harus membayar biaya transaksi yang besar. Kesulitan untuk melakukan transaksi seperti itu dapat disebut Illiquidity. Berikut beberapa literatur yang menemukan bahwa Liquidity (dan Illiquidity) memiliki dampak terhadap performa saham:
Sebuah saham yang sulit untuk ditransaksikan menyebabkan investor untuk harus membayar biaya premium, yaitu sebuah biaya tambahan yang harus dibayar untuk dapat melakukan transaksi secara langsung. Ini dapat menyebabkan investor untuk membeli saham dengan harga yang lebih mahal daripada harga seharusnya, atau mendapatkan hasil penjualan saham yang lebih rendah. Oleh karena itu, investor mengharapkan keuntungan dari investasi yang lebih besar sebagai kompensasi untuk biaya transaksi yang harus dibayarkan. Literatur ini juga menyarankan para investor jangka pendek untuk mencari saham dengan Liquidity yang tinggi untuk memaksimalkan pendapatan, dan menyarankan para investor jangka panjang untuk mencari saham dengan Liquidity rendah untuk dipegang untuk waktu yang lama dengan tujuan mengimbangi biaya transaksi yang dibayarkan (Amihud & Mendelson, 1986).
Saham dengan Liquidity yang rendah terlihat mengalami pergerakan harga yang lebih besar dibandingkan saham dengan Liquidity yang tinggi ketika terjadi sebuah transaksi. Saham dari perusahaan kecil cenderung memiliki Liquidity yang rendah dan perubahan harga yang lebih besar dibandingkan saham dari perusahaan besar, yang cenderung memiliki Liquidity yang tinggi (Amihud & Mendelson, 1986; Pástor & Stambaugh, 2003).
Liquidity juga berhubungan dengan seberapa cepat sebuah transaksi dilakukan. Pada sebuah riset yang dilakukan pada London Stock Exchange, para peneliti menemukan bahwa jika transaksi tidak segera dilakukan saat munculnya kondisi untuk melakukan transaksi yang optimal, keuntungan yang diperoleh investor akan turun secara drastis. Bahkan, keuntungan yang diperoleh bisa tereliminasi setelah memfaktorkan biaya transaksi (Bowen et al., 2010).
Liquidity juga berhubungan dengan jumlah saham yang ditransaksikan.Riset pertama yang mencoba meneliti hubungan antara keuntungan investasi dengan jumlah transaksi yang terjadi menggunakan selisih antara harga tertinggi yang rela dibayarkan oleh pihak pembeli saham dengan harga terendah yang rela diterima oleh pihak penjual. Riset tersebut menemukan bahwa semakin besar selisih tersebut, semakin tinggi juga keuntungan yang diperoleh oleh investor (Amihud & Mendelson, 1986). Terdapat juga riset berbeda yang menggunakan Turnover Rate (ratio jumlah saham yang ditransaksikan terhadap jumlah saham yang beredar di pasar saham) sebagai perwakilan jumlah transaksi yang dilakukan. Riset kedua tersebut menemukan bahwa terdapat hubungan korelasi negatif antara performa saham dengan turnover rate (Datar et al., 1998).
Pada artikel ini, Penulis akan menggunakan pengukuran Liquidity yang berbeda juga, yaitu Amihud Illiquidity Measure, yang merupakan metode pengukuran dampak jumlah saham yang ditransaksikan terhadap harga saham tersebut, sebagai perwakilan Liquidity. Pengukuran Amihud Illiquidity Measure ini didapatkan dengan mencari hasil pembagian antara Daily Return dengan Daily Dollar Volume. Daily Dollar Volume sendiri merupakan hasil perkalian jumlah transaksi saham pada sebuah hari dengan harga penutupan saham pada hari yang sama (Amihud, 2002).
Dengan adanya faktor Liquidity sebagai resiko, maka ini akan membuat investor mengharapakan return yang lebih besar untuk sahamnya sebagai bentuk kompensasi untuk menanggung kemungkinan investor tidak dapat melakukan transasksi. Oleh karena itu, berikut hipotesa yang dibuat untuk artikel ini. H0: Daily Return sebuah saham tidak memiliki korelasi dengan Liquidity H1: Daily Return sebuah saham memiliki korelasi dengan Liquidity
Pada artikel ini, data yang digunakan adalah saham-saham yang terdaftar pada Bursa Efek Indonesia (BEI) dan jangka waktu yang dianalisa adalah dari Januari 2012 hingga Desember 2022. Ada juga syarat yang diberlakukan dalam pemilihan saham yang dijadikan sample, yaitu saham sudah harus terdaftar dari Januari 2012 sampai Desember 2022. Selain itu, penulis juga memastikan bahwa saham-saham yang dipilih tidak bertahan pada harga yang sama untuk lebih dari 1 tahun. Pada jangka waktu Januari 2012 hingga Desember 2022, diketahui bahwa terdapat 810 saham perusahaan yang terdaftar pada BEI. Dari jumlah tersebut, 113 saham dari berbagai sektor saham diambil menjadi sample yang diobservasi untuk artikel ini. Jenis data yang digunakan adalah data harian harga saham dan meliputi hal-hal sebagai berikut: Tanggal (Date), Harga Pembukaan (Open), Harga Tertinggi (High), Harga Terendah (Low), Harga Penutupan (Close), Harga Penutupan yang Disesuaikan (Adj. Close), dan Jumlah Transaksi (Volume). Data harga saham juga didapatkan dari situs finance.yahoo.com.
Situs finance.yahoo.com menyediakan data harga saham harian, bulanan, dan tahunan dalam bentuk file “.csv”. Seperti yang dibahas sebelumnya, pada artikel ini data yang digunakan dalam bentuk harian untuk melihat hubungan korelasi antara Daily Return dan Liquidity. Pada tahap ini, akan dilakukan pengubahan tipe data untuk mempermudah langkah-langkah selanjutnya. Selain itu, dilakukan juga pengecekan missing values untuk melihat apakah terdapat missing values (NA) atau tidak.
# PT Astra Agro Lestari (AALI)
AALI <- read.csv(file="Data Saham/AALI.JK.csv")
AALI$Date <- as.Date(AALI$Date, format="%Y-%m-%d")
AALI$Close <- as.numeric(AALI$Close)
AALI$Volume <- as.integer(AALI$Volume)
str(AALI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 20725 20772 20915 20582 20725 ...
## $ High : num 20772 20867 21058 21010 20867 ...
## $ Low : num 20486 20486 20629 20582 20486 ...
## $ Close : num 20725 20772 20915 20582 20725 ...
## $ Adj.Close: num 15481 15517 15623 15374 15481 ...
## $ Volume : int 69790 282310 833813 831189 164243 793408 692133 740934 1803010 3 ...colSums(is.na(AALI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Mahaka Media Tbk (ABBA)
ABBA <- read.csv(file="Data Saham/ABBA.JK.csv")
ABBA$Date <- as.Date(ABBA$Date, format="%Y-%m-%d")
ABBA$Close <- as.numeric(ABBA$Close)## Warning: NAs introduced by coercionABBA$Volume <- as.integer(ABBA$Volume)## Warning: NAs introduced by coercionstr(ABBA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "121.000000" "119.000000" "118.000000" "119.000000" ...
## $ High : chr "121.000000" "121.000000" "120.000000" "119.000000" ...
## $ Low : chr "119.000000" "119.000000" "118.000000" "118.000000" ...
## $ Close : num 121 119 118 119 123 123 118 118 119 119 ...
## $ Adj.Close: chr "121.000000" "119.000000" "118.000000" "119.000000" ...
## $ Volume : int 11060000 464500 164000 164500 11383000 152000 617500 162000 30000 0 ...colSums(is.na(ABBA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT ABM Investama Tbk (ABMM)
ABMM <- read.csv(file="Data Saham/ABMM.JK.csv")
ABMM$Date <- as.Date(ABMM$Date, format="%Y-%m-%d")
ABMM$Close <- as.numeric(ABMM$Close)## Warning: NAs introduced by coercionABMM$Volume <- as.integer(ABMM$Volume)## Warning: NAs introduced by coercionstr(ABMM)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "3800.000000" "3800.000000" "3775.000000" "3775.000000" ...
## $ High : chr "3800.000000" "3800.000000" "3800.000000" "3775.000000" ...
## $ Low : chr "3775.000000" "3750.000000" "3775.000000" "3775.000000" ...
## $ Close : num 3800 3800 3775 3775 3775 ...
## $ Adj.Close: chr "3373.643555" "3373.643555" "3351.448730" "3351.448730" ...
## $ Volume : int 1298500 683000 743500 24000 73500 1921000 651500 1141000 1302000 3 ...colSums(is.na(ABMM))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Ace Hardware Indonesia Tbk (ACES)
ACES <- read.csv(file="Data Saham/ACES.JK.csv")
ACES$Date <- as.Date(ACES$Date, format="%Y-%m-%d")
ACES$Close <- as.numeric(ACES$Close)## Warning: NAs introduced by coercionACES$Volume <- as.integer(ACES$Volume)## Warning: NAs introduced by coercionstr(ACES)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "415.000000" "420.000000" "415.000000" "420.000000" ...
## $ High : chr "415.000000" "420.000000" "417.500000" "420.000000" ...
## $ Low : chr "415.000000" "420.000000" "415.000000" "420.000000" ...
## $ Close : num 415 420 415 420 418 ...
## $ Adj.Close: chr "348.266876" "352.462860" "348.266876" "352.462860" ...
## $ Volume : int 135000 210000 10000 2325000 555000 555000 1750000 2615000 5645000 30 ...colSums(is.na(ACES))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1#PT Akasha Wira International Tbk (ADES)
ADES <- read.csv(file="Data Saham/ADES.JK.csv")
ADES$Date <- as.Date(ADES$Date, format="%Y-%m-%d")
ADES$Close <- as.numeric(ADES$Close)## Warning: NAs introduced by coercionADES$Volume <- as.integer(ADES$Volume)## Warning: NAs introduced by coercionstr(ADES)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1020.000000" "1020.000000" "1010.000000" "1020.000000" ...
## $ High : chr "1100.000000" "1030.000000" "1020.000000" "1030.000000" ...
## $ Low : chr "1010.000000" "1020.000000" "1010.000000" "1020.000000" ...
## $ Close : num 1020 1020 1010 1020 1010 1010 1090 1080 1060 1060 ...
## $ Adj.Close: chr "1020.000000" "1020.000000" "1010.000000" "1020.000000" ...
## $ Volume : int 9000 21500 52000 53500 62000 13500 2790500 333500 84500 0 ...colSums(is.na(ADES))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1#PT Adhi Karya (Persero) Tbk (ADHI)
ADHI <- read.csv(file="Data Saham/ADHI.JK.csv")
ADHI$Date <- as.Date(ADHI$Date, format="%Y-%m-%d")
ADHI$Close <- as.numeric(ADHI$Close)## Warning: NAs introduced by coercionADHI$Volume <- as.integer(ADHI$Volume)## Warning: NAs introduced by coercionstr(ADHI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "492.110535" "500.595184" "509.079865" "500.595184" ...
## $ High : chr "492.110535" "500.595184" "517.564514" "509.079865" ...
## $ Low : chr "483.625885" "483.625885" "500.595184" "492.110535" ...
## $ Close : num 492 501 509 501 501 ...
## $ Adj.Close: chr "423.129486" "430.424774" "437.720093" "430.424774" ...
## $ Volume : int 298185 9709871 10102344 7384499 2057830 671800 7388035 2381355 2866347 3 ...colSums(is.na(ADHI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1#PT Polychem Indonesia (ADMG)
ADMG <- read.csv(file="Data Saham/ADMG.JK.csv")
ADMG$Date <- as.Date(ADMG$Date, format="%Y-%m-%d")
ADMG$Close <- as.numeric(ADMG$Close)
ADMG$Volume <- as.integer(ADMG$Volume)
str(ADMG)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 570 570 610 600 600 600 600 590 620 620 ...
## $ High : num 570 580 630 620 610 600 610 610 640 630 ...
## $ Low : num 560 560 570 590 590 590 590 590 590 610 ...
## $ Close : num 570 570 610 600 600 600 600 590 620 620 ...
## $ Adj.Close: num 570 570 610 600 600 600 600 590 620 620 ...
## $ Volume : int 2338000 3100000 51575500 12825500 5805500 2279000 9475500 3675000 32741500 0 ...colSums(is.na(ADMG))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0#PT Adaro Energy Tbk (ADRO)
ADRO <- read.csv(file="Data Saham/ADRO.JK.csv")
ADRO$Date <- as.Date(ADRO$Date, format="%Y-%m-%d")
ADRO$Close <- as.numeric(ADRO$Close)## Warning: NAs introduced by coercionADRO$Volume <- as.integer(ADRO$Volume)## Warning: NAs introduced by coercionstr(ADRO)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1760.000000" "1810.000000" "1830.000000" "1790.000000" ...
## $ High : chr "1770.000000" "1820.000000" "1830.000000" "1830.000000" ...
## $ Low : chr "1750.000000" "1770.000000" "1810.000000" "1780.000000" ...
## $ Close : num 1760 1810 1830 1790 1770 1780 1820 1810 1810 1810 ...
## $ Adj.Close: chr "919.151917" "945.264282" "955.709045" "934.819214" ...
## $ Volume : int 6244000 109473500 69199000 56594500 33691500 48736000 83169000 44526500 26521000 0 ...colSums(is.na(ADRO))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1#PT Bank Raya Indonesia (AGRO)
AGRO <- read.csv(file="Data Saham/AGRO.JK.csv")
AGRO$Date <- as.Date(AGRO$Date, format="%Y-%m-%d")
AGRO$Close <- as.numeric(AGRO$Close)## Warning: NAs introduced by coercionAGRO$Volume <- as.integer(AGRO$Volume)## Warning: NAs introduced by coercionstr(AGRO)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "108.362442" "109.273048" "108.362442" "108.362442" ...
## $ High : chr "108.362442" "110.183662" "109.273048" "110.183662" ...
## $ Low : chr "107.451836" "108.362442" "107.451836" "108.362442" ...
## $ Close : num 108 109 108 108 109 ...
## $ Adj.Close: chr "103.931145" "104.804527" "103.931145" "103.931145" ...
## $ Volume : int 128485 119151 143859 121896 50515 320115 332744 217986 380514 3 ...colSums(is.na(AGRO))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1#PT FKS Food Sejahtera (AISA)
AISA <- read.csv(file="Data Saham/AISA.JK.csv")
AISA$Date <- as.Date(AISA$Date, format="%Y-%m-%d")
AISA$Close <- as.numeric(AISA$Close)
AISA$Volume <- as.integer(AISA$Volume)
str(AISA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 490 490 480 470 455 465 475 470 470 475 ...
## $ High : num 490 495 490 485 470 475 475 475 470 475 ...
## $ Low : num 485 485 480 465 445 440 465 465 460 465 ...
## $ Close : num 490 490 480 470 455 465 475 470 470 475 ...
## $ Adj.Close: num 481 481 471 461 446 ...
## $ Volume : int 2814500 6858500 13861000 13625000 26350500 29284500 19772500 7026000 9113000 3 ...colSums(is.na(AISA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0#PT Argha Karya Prima Industry Tbk (AKPI)
AKPI <- read.csv(file="Data Saham/AKPI.JK.csv")
AKPI$Date <- as.Date(AKPI$Date, format="%Y-%m-%d")
AKPI$Close <- as.numeric(AKPI$Close)## Warning: NAs introduced by coercionAKPI$Volume <- as.integer(AKPI$Volume)## Warning: NAs introduced by coercionstr(AKPI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1060.000000" "1100.000000" "1070.000000" "1070.000000" ...
## $ High : chr "1070.000000" "1100.000000" "1090.000000" "1100.000000" ...
## $ Low : chr "1000.000000" "1060.000000" "1070.000000" "1050.000000" ...
## $ Close : num 1060 1100 1070 1070 1070 1070 1070 1070 1070 1070 ...
## $ Adj.Close: chr "861.770386" "894.289978" "869.900330" "869.900330" ...
## $ Volume : int 105500 44000 28000 63000 12500 14000 60000 2500 25000 0 ...colSums(is.na(AKPI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1#PT AKR Corporindo Tbk (AKRA)
AKRA <- read.csv(file="Data Saham/AKRA.JK.csv")
AKRA$Date <- as.Date(AKRA$Date, format="%Y-%m-%d")
AKRA$Close <- as.numeric(AKRA$Close)
AKRA$Volume <- as.integer(AKRA$Volume)
str(AKRA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 605 615 635 660 670 675 680 660 665 670 ...
## $ High : num 610 620 650 660 680 675 680 685 670 670 ...
## $ Low : num 600 605 615 630 660 665 660 650 655 655 ...
## $ Close : num 605 615 635 660 670 675 680 660 665 670 ...
## $ Adj.Close: num 443 450 465 483 491 ...
## $ Volume : int 27332500 76372500 135265000 104852500 64517500 29925000 70777500 83502500 113357500 15 ...colSums(is.na(AKRA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0#PT Mineral Sumberdaya Mandiri (AKSI)
AKSI <- read.csv(file="Data Saham/AKSI.JK.csv")
AKSI$Date <- as.Date(AKSI$Date, format="%Y-%m-%d")
AKSI$Close <- as.numeric(AKSI$Close)## Warning: NAs introduced by coercionAKSI$Volume <- as.integer(AKSI$Volume)## Warning: NAs introduced by coercionstr(AKSI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "75.000000" "75.000000" "75.000000" "75.000000" ...
## $ High : chr "75.000000" "75.000000" "75.000000" "75.000000" ...
## $ Low : chr "75.000000" "75.000000" "75.000000" "75.000000" ...
## $ Close : num 75 75 75 75 75 75 75 75 75 75 ...
## $ Adj.Close: chr "75.000000" "75.000000" "75.000000" "75.000000" ...
## $ Volume : int 3000 3000 3000 3000 3000 3000 3000 3000 3000 0 ...colSums(is.na(AKSI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1#PT Alkindo Naratama Tbk (ALDO)
ALDO <- read.csv(file="Data Saham/ALDO.JK.csv")
ALDO$Date <- as.Date(ALDO$Date, format="%Y-%m-%d")
ALDO$Close <- as.numeric(ALDO$Close)## Warning: NAs introduced by coercionALDO$Volume <- as.integer(ALDO$Volume)## Warning: NAs introduced by coercionstr(ALDO)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "212.244965" "212.244965" "212.244965" "212.244965" ...
## $ High : chr "224.373245" "218.309097" "218.309097" "218.309097" ...
## $ Low : chr "209.212891" "212.244965" "212.244965" "212.244965" ...
## $ Close : num 212 212 212 212 215 ...
## $ Adj.Close: chr "208.756653" "208.756653" "208.756653" "208.756653" ...
## $ Volume : int 14483500 8360622 17478153 13560039 11337960 10337819 8576646 8435653 9117531 1 ...colSums(is.na(ALDO))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Alumindo Light Metal Industry (ALMI)
ALMI <- read.csv(file="Data Saham/ALMI.JK.csv")
ALMI$Date <- as.Date(ALMI$Date, format="%Y-%m-%d")
ALMI$Close <- as.numeric(ALMI$Close)
ALMI$Volume <- as.integer(ALMI$Volume)
str(ALMI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 460 450 465 485 475 475 480 480 480 485 ...
## $ High : num 460 465 465 500 480 475 480 495 495 500 ...
## $ Low : num 450 450 455 455 470 470 475 480 480 485 ...
## $ Close : num 460 450 465 485 475 475 480 480 480 485 ...
## $ Adj.Close: num 391 382 395 412 403 ...
## $ Volume : int 52000 101000 146000 565000 131000 37000 328000 200000 5000 6 ...colSums(is.na(ALMI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Asahimas Flat Glass Tbk (AMFG)
AMFG <- read.csv(file="Data Saham/AMFG.JK.csv")
AMFG$Date <- as.Date(AMFG$Date, format="%Y-%m-%d")
AMFG$Close <- as.numeric(AMFG$Close)
AMFG$Volume <- as.integer(AMFG$Volume)
str(AMFG)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 6450 6300 6400 6400 6400 6400 6400 6450 6500 6450 ...
## $ High : num 6600 6450 6450 6400 6500 6400 6400 6450 6500 6500 ...
## $ Low : num 6450 6300 6400 6400 6400 6400 6350 6400 6350 6450 ...
## $ Close : num 6450 6300 6400 6400 6400 6400 6400 6450 6500 6450 ...
## $ Adj.Close: num 5860 5724 5815 5815 5815 ...
## $ Volume : int 5000 227000 35500 10000 14000 1500 14000 10000 54500 3 ...colSums(is.na(AMFG))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Sumber Alfaria Trijaya Tbk (AMRT)
AMRT <- read.csv(file="Data Saham/AMRT.JK.csv")
AMRT$Date <- as.Date(AMRT$Date, format="%Y-%m-%d")
AMRT$Close <- as.numeric(AMRT$Close)## Warning: NAs introduced by coercionAMRT$Volume <- as.integer(AMRT$Volume)## Warning: NAs introduced by coercionstr(AMRT)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "395.000000" "395.000000" "395.000000" "395.000000" ...
## $ High : chr "395.000000" "395.000000" "395.000000" "395.000000" ...
## $ Low : chr "395.000000" "395.000000" "395.000000" "395.000000" ...
## $ Close : num 395 395 395 395 380 365 380 380 395 395 ...
## $ Adj.Close: chr "356.323334" "356.323334" "356.323334" "356.323334" ...
## $ Volume : int 225000 10000 200000 8805000 10000 10000 1515000 2585000 630000 0 ...colSums(is.na(AMRT))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 2 0 2# PT Aneka Tambang Tbk (ANTM)
ANTM <- read.csv(file="Data Saham/ANTM.JK.csv")
ANTM$Date <- as.Date(ANTM$Date, format="%Y-%m-%d")
ANTM$Close <- as.numeric(ANTM$Close)## Warning: NAs introduced by coercionANTM$Volume <- as.integer(ANTM$Volume)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion to integer rangestr(ANTM)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1360.762451" "1369.162231" "1394.361572" "1394.361572" ...
## $ High : chr "1369.162231" "1369.162231" "1402.761353" "1402.761353" ...
## $ Low : chr "1352.362671" "1360.762451" "1369.162231" "1377.562012" ...
## $ Close : num 1361 1369 1394 1394 1386 ...
## $ Adj.Close: chr "1162.576050" "1169.752563" "1191.281738" "1191.281738" ...
## $ Volume : int 2292325 4413812 18548725 14637903 4555482 4679890 15465902 21811315 13202744 3 ...colSums(is.na(ANTM))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 2# PT Agung Podomoro Land Tbk(APLN)
APLN <- read.csv(file="Data Saham/APLN.JK.csv")
APLN$Date <- as.Date(APLN$Date, format="%Y-%m-%d")
APLN$Close <- as.numeric(APLN$Close)## Warning: NAs introduced by coercionAPLN$Volume <- as.integer(APLN$Volume)## Warning: NAs introduced by coercionstr(APLN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "350.000000" "360.000000" "355.000000" "355.000000" ...
## $ High : chr "355.000000" "360.000000" "360.000000" "370.000000" ...
## $ Low : chr "345.000000" "350.000000" "350.000000" "355.000000" ...
## $ Close : num 350 360 355 355 355 355 355 360 360 360 ...
## $ Adj.Close: chr "327.303436" "336.654968" "331.979187" "331.979187" ...
## $ Volume : int 2130500 1622000 4866000 18270000 3654000 2492500 2288500 14080000 9752500 0 ...colSums(is.na(APLN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Astra International Tbk (ASII)
ASII <- read.csv(file="Data Saham/ASII.JK.csv")
ASII$Date <- as.Date(ASII$Date, format="%Y-%m-%d")
ASII$Close <- as.numeric(ASII$Close)## Warning: NAs introduced by coercionASII$Volume <- as.integer(ASII$Volume)## Warning: NAs introduced by coercionstr(ASII)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "7370.000000" "7500.000000" "7715.000000" "7745.000000" ...
## $ High : chr "7400.000000" "7500.000000" "7790.000000" "7860.000000" ...
## $ Low : chr "7350.000000" "7400.000000" "7595.000000" "7620.000000" ...
## $ Close : num 7370 7500 7715 7745 7730 ...
## $ Adj.Close: chr "5337.428711" "5431.576172" "5587.281738" "5609.006836" ...
## $ Volume : int 2365000 16420000 25860000 22700000 9035000 21740000 15960000 21145000 20330000 30 ...colSums(is.na(ASII))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Alam Sutera Realty (ASRI)
ASRI <- read.csv(file="Data Saham/ASRI.JK.csv")
ASRI$Date <- as.Date(ASRI$Date, format="%Y-%m-%d")
ASRI$Close <- as.numeric(ASRI$Close)## Warning: NAs introduced by coercionASRI$Volume <- as.integer(ASRI$Volume)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion to integer rangestr(ASRI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "460.000000" "465.000000" "480.000000" "485.000000" ...
## $ High : chr "465.000000" "470.000000" "490.000000" "495.000000" ...
## $ Low : chr "455.000000" "460.000000" "460.000000" "475.000000" ...
## $ Close : num 460 465 480 485 490 500 495 490 480 480 ...
## $ Adj.Close: chr "431.275848" "435.963593" "450.026947" "454.714691" ...
## $ Volume : int 7218500 12665000 210967500 174111000 179704000 202216500 130036000 33750000 174432000 0 ...colSums(is.na(ASRI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 2# PT Astra Otoparts (AUTO)
AUTO <- read.csv(file="Data Saham/AUTO.JK.csv")
AUTO$Date <- as.Date(AUTO$Date, format="%Y-%m-%d")
AUTO$Close <- as.numeric(AUTO$Close)## Warning: NAs introduced by coercionAUTO$Volume <- as.integer(AUTO$Volume)## Warning: NAs introduced by coercionstr(AUTO)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "3212.563232" "3212.563232" "3380.383789" "3356.409424" ...
## $ High : chr "3236.537598" "3236.537598" "3380.383789" "3500.255615" ...
## $ Low : chr "3212.563232" "3212.563232" "3236.537598" "3356.409424" ...
## $ Close : num 3213 3213 3380 3356 3380 ...
## $ Adj.Close: chr "2223.687988" "2223.687988" "2339.850342" "2323.255127" ...
## $ Volume : int 94893 93328 1430174 1024010 304492 125655 238796 546938 198128 3 ...colSums(is.na(AUTO))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bank MNC Internasional Tbk(BABP)
BABP <- read.csv(file="Data Saham/BABP.JK.csv")
BABP$Date <- as.Date(BABP$Date, format="%Y-%m-%d")
BABP$Close <- as.numeric(BABP$Close)
BABP$Volume <- as.integer(BABP$Volume)## Warning: NAs introduced by coercion to integer rangestr(BABP)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 95.9 95.9 95.9 95.9 95.9 ...
## $ High : num 95.9 95.9 95.9 95.9 95.9 ...
## $ Low : num 95.9 95.9 95.9 95.9 95.9 ...
## $ Close : num 95.9 95.9 95.9 95.9 95.9 ...
## $ Adj.Close: num 95.9 95.9 95.9 95.9 95.9 ...
## $ Volume : int 552 552 552 552 552 10496 10496 10496 10496 0 ...colSums(is.na(BABP))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 3# PT Bekasi Asri Pemula Tbk (BAPA)
BAPA <- read.csv(file="Data Saham/BAPA.JK.csv")
BAPA$Date <- as.Date(BAPA$Date, format="%Y-%m-%d")
BAPA$Close <- as.numeric(BAPA$Close)## Warning: NAs introduced by coercionBAPA$Volume <- as.integer(BAPA$Volume)## Warning: NAs introduced by coercionstr(BAPA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "148.000000" "148.000000" "148.000000" "148.000000" ...
## $ High : chr "148.000000" "148.000000" "148.000000" "148.000000" ...
## $ Low : chr "148.000000" "148.000000" "148.000000" "148.000000" ...
## $ Close : num 148 148 148 148 148 148 148 148 148 148 ...
## $ Adj.Close: chr "148.000000" "148.000000" "148.000000" "148.000000" ...
## $ Volume : int 500 500 1000 500 5500 5500 5500 10000 10000 0 ...colSums(is.na(BAPA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bayu Buana Tbk (BAYU)
BAYU <- read.csv(file="Data Saham/BAYU.JK (1).csv")
BAYU$Date <- as.Date(BAYU$Date, format="%Y-%m-%d")
BAYU$Close <- as.numeric(BAYU$Close)## Warning: NAs introduced by coercionBAYU$Volume <- as.integer(BAYU$Volume)## Warning: NAs introduced by coercionstr(BAYU)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "250.000000" "255.000000" "250.000000" "250.000000" ...
## $ High : chr "250.000000" "255.000000" "255.000000" "255.000000" ...
## $ Low : chr "250.000000" "245.000000" "250.000000" "250.000000" ...
## $ Close : num 250 255 250 250 250 255 260 260 260 255 ...
## $ Adj.Close: chr "218.582275" "222.953918" "218.582275" "218.582275" ...
## $ Volume : int 30500 157500 313500 82500 573500 72500 127500 232500 36500 3 ...colSums(is.na(BAYU))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bank Central Asia Tbk (BBCA)
BBCA <- read.csv(file="Data Saham/BBCA.JK.csv")
BBCA$Date <- as.Date(BBCA$Date, format="%Y-%m-%d")
BBCA$Close <- as.numeric(BBCA$Close)
BBCA$Volume <- as.integer(BBCA$Volume)
str(BBCA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 1600 1620 1620 1610 1600 1630 1630 1620 1630 1600 ...
## $ High : num 1600 1620 1630 1630 1610 1630 1640 1650 1640 1630 ...
## $ Low : num 1590 1590 1600 1610 1590 1580 1620 1610 1610 1600 ...
## $ Close : num 1600 1620 1620 1610 1600 1630 1630 1620 1630 1600 ...
## $ Adj.Close: num 1376 1393 1393 1384 1376 ...
## $ Volume : int 7870000 27775000 87245000 57197500 27190000 46510000 49875000 73070000 63142500 15 ...colSums(is.na(BBCA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Bank Rakyat Indonesia Persero (BBRI)
BBRI <- read.csv(file="Data Saham/BBRI.JK (1).csv")
BBRI$Date <- as.Date(BBRI$Date, format="%Y-%m-%d")
BBRI$Close <- as.numeric(BBRI$Close)## Warning: NAs introduced by coercionBBRI$Volume <- as.integer(BBRI$Volume)## Warning: NAs introduced by coercionstr(BBRI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1360.000000" "1380.000000" "1390.000000" "1390.000000" ...
## $ High : chr "1370.000000" "1390.000000" "1390.000000" "1400.000000" ...
## $ Low : chr "1350.000000" "1360.000000" "1370.000000" "1370.000000" ...
## $ Close : num 1360 1380 1390 1390 1380 1400 1410 1400 1380 1370 ...
## $ Adj.Close: chr "1001.109924" "1015.832092" "1023.193298" "1023.193298" ...
## $ Volume : int 15835000 81980000 151180000 134337500 105145000 116387500 97522500 160435000 132167500 15 ...colSums(is.na(BBRI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bank Tabungan Negara Tbk (BBTN)
BBTN <- read.csv(file="Data Saham/BBTN.JK.csv")
BBTN$Date <- as.Date(BBTN$Date, format="%Y-%m-%d")
BBTN$Close <- as.numeric(BBTN$Close)## Warning: NAs introduced by coercionBBTN$Volume <- as.integer(BBTN$Volume)## Warning: NAs introduced by coercionstr(BBTN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1163.181885" "1172.795044" "1163.181885" "1163.181885" ...
## $ High : chr "1163.181885" "1182.408081" "1172.795044" "1172.795044" ...
## $ Low : chr "1153.568848" "1153.568848" "1153.568848" "1153.568848" ...
## $ Close : num 1163 1173 1163 1163 1163 ...
## $ Adj.Close: chr "982.050842" "990.167114" "982.050842" "982.050842" ...
## $ Volume : int 1624870 2792031 5411900 3526447 1864128 5361968 12236980 17319642 32883342 0 ...colSums(is.na(BBTN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bank Danamon Indonesia (BDMN)
BDMN <- read.csv(file="Data Saham/BDMN.JK.csv")
BDMN$Date <- as.Date(BDMN$Date, format="%Y-%m-%d")
BDMN$Close <- as.numeric(BDMN$Close)## Warning: NAs introduced by coercionBDMN$Volume <- as.integer(BDMN$Volume)## Warning: NAs introduced by coercionstr(BDMN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "4050.000000" "4150.000000" "4275.000000" "4300.000000" ...
## $ High : chr "4100.000000" "4150.000000" "4325.000000" "4325.000000" ...
## $ Low : chr "4050.000000" "4075.000000" "4175.000000" "4275.000000" ...
## $ Close : num 4050 4150 4275 4300 4350 ...
## $ Adj.Close: chr "2671.040039" "2736.991211" "2819.431152" "2835.919434" ...
## $ Volume : int 1163500 1737000 9747000 4762500 7678000 30019500 10691000 13551000 1682500 3 ...colSums(is.na(BDMN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT BFI Finance Indonesia (BFIN)
BFIN <- read.csv(file="Data Saham/BFIN.JK.csv")
BFIN$Date <- as.Date(BFIN$Date, format="%Y-%m-%d")
BFIN$Close <- as.numeric(BFIN$Close)## Warning: NAs introduced by coercionBFIN$Volume <- as.integer(BFIN$Volume)## Warning: NAs introduced by coercionstr(BFIN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "285.000000" "277.500000" "277.500000" "277.500000" ...
## $ High : chr "285.000000" "277.500000" "277.500000" "277.500000" ...
## $ Low : chr "285.000000" "277.500000" "277.500000" "277.500000" ...
## $ Close : num 285 278 278 278 278 ...
## $ Adj.Close: chr "165.051071" "160.707642" "160.707642" "160.707642" ...
## $ Volume : int 13580000 8530000 8500000 8500000 8500000 8500000 100000 100000 40000 0 ...colSums(is.na(BFIN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT MNC Investama Tbk (BHIT)
BHIT <- read.csv(file="Data Saham/BHIT.JK (1).csv")
BHIT$Date <- as.Date(BHIT$Date, format="%Y-%m-%d")
BHIT$Close <- as.numeric(BHIT$Close)
BHIT$Volume <- as.integer(BHIT$Volume)## Warning: NAs introduced by coercion to integer rangestr(BHIT)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 290 295 290 285 285 285 285 285 285 280 ...
## $ High : num 295 295 295 290 285 285 285 285 285 280 ...
## $ Low : num 280 285 285 280 280 280 280 275 275 275 ...
## $ Close : num 290 295 290 285 285 285 285 285 285 280 ...
## $ Adj.Close: num 278 283 278 273 273 ...
## $ Volume : int 15698000 29876000 17106000 17334000 15918000 3097500 9303500 30176000 13228000 3 ...colSums(is.na(BHIT))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 13# PT Bhuwanatala Indah Permai Tbk (BIPP)
BIPP <- read.csv(file="Data Saham/BIPP.JK.csv")
BIPP$Date <- as.Date(BIPP$Date, format="%Y-%m-%d")
BIPP$Close <- as.numeric(BIPP$Close)## Warning: NAs introduced by coercionBIPP$Volume <- as.integer(BIPP$Volume)## Warning: NAs introduced by coercionstr(BIPP)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "50.000000" "56.000000" "54.000000" "54.000000" ...
## $ High : chr "52.000000" "59.000000" "58.000000" "55.000000" ...
## $ Low : chr "50.000000" "51.000000" "54.000000" "54.000000" ...
## $ Close : num 50 56 54 54 53 52 52 50 50 50 ...
## $ Adj.Close: chr "50.000000" "56.000000" "54.000000" "54.000000" ...
## $ Volume : int 3336500 51320500 8186500 3868500 3437500 3146500 3940000 7097500 3664000 4 ...colSums(is.na(BIPP))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT BISI International Tbk (BISI)
BISI <- read.csv(file="Data Saham/BISI.JK.csv")
BISI$Date <- as.Date(BISI$Date, format="%Y-%m-%d")
BISI$Close <- as.numeric(BISI$Close)## Warning: NAs introduced by coercionBISI$Volume <- as.integer(BISI$Volume)## Warning: NAs introduced by coercionstr(BISI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "910.000000" "950.000000" "940.000000" "950.000000" ...
## $ High : chr "920.000000" "950.000000" "960.000000" "960.000000" ...
## $ Low : chr "900.000000" "910.000000" "930.000000" "940.000000" ...
## $ Close : num 910 950 940 950 940 930 940 940 940 960 ...
## $ Adj.Close: chr "625.952515" "653.466919" "646.588196" "653.466919" ...
## $ Volume : int 309500 4757500 2976000 1943000 1047500 1014500 810500 437500 308500 3 ...colSums(is.na(BISI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bukit Darmo Property Tbk (BKDP)
BKDP <- read.csv(file="Data Saham/BKDP.JK.csv")
BKDP$Date <- as.Date(BKDP$Date, format="%Y-%m-%d")
BKDP$Close <- as.numeric(BKDP$Close)## Warning: NAs introduced by coercionBKDP$Volume <- as.integer(BKDP$Volume)## Warning: NAs introduced by coercionstr(BKDP)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "117.000000" "117.000000" "119.000000" "119.000000" ...
## $ High : chr "117.000000" "120.000000" "119.000000" "119.000000" ...
## $ Low : chr "117.000000" "117.000000" "117.000000" "117.000000" ...
## $ Close : num 117 117 119 119 119 115 117 116 117 116 ...
## $ Adj.Close: chr "117.000000" "117.000000" "119.000000" "119.000000" ...
## $ Volume : int 50000 84500 28000 102000 370000 344000 52000 10000 11000 3 ...colSums(is.na(BKDP))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Sentul City Tbk (BKSL)
BKSL <- read.csv(file="Data Saham/BKSL.JK.csv")
BKSL$Date <- as.Date(BKSL$Date, format="%Y-%m-%d")
BKSL$Close <- as.numeric(BKSL$Close)## Warning: NAs introduced by coercionBKSL$Volume <- as.integer(BKSL$Volume)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion to integer rangestr(BKSL)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "275.000000" "275.000000" "265.000000" "265.000000" ...
## $ High : chr "285.000000" "280.000000" "275.000000" "270.000000" ...
## $ Low : chr "260.000000" "270.000000" "260.000000" "260.000000" ...
## $ Close : num 275 275 265 265 265 270 260 260 260 260 ...
## $ Adj.Close: chr "274.094116" "274.094116" "264.127075" "264.127075" ...
## $ Volume : int 240007500 46537000 88853000 103734000 68159500 113863500 119178000 40278500 61982000 0 ...colSums(is.na(BKSL))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 5# PT Bank Mandiri Tbk (BMRI)
BMRI <- read.csv(file="Data Saham/BMRI.JK.csv")
BMRI$Date <- as.Date(BMRI$Date, format="%Y-%m-%d")
BMRI$Close <- as.numeric(BMRI$Close)## Warning: NAs introduced by coercionBMRI$Volume <- as.integer(BMRI$Volume)## Warning: NAs introduced by coercionstr(BMRI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "3350.000000" "3400.000000" "3425.000000" "3375.000000" ...
## $ High : chr "3375.000000" "3400.000000" "3450.000000" "3425.000000" ...
## $ Low : chr "3325.000000" "3350.000000" "3400.000000" "3375.000000" ...
## $ Close : num 3350 3400 3425 3375 3350 ...
## $ Adj.Close: chr "2391.283447" "2426.974365" "2444.819824" "2409.128906" ...
## $ Volume : int 10288000 44228000 78827000 40732000 27621000 40428000 76699000 49087000 50518000 6 ...colSums(is.na(BMRI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Global Mediacom (BMTR)
BMTR <- read.csv(file="Data Saham/BMTR.JK (1).csv")
BMTR$Date <- as.Date(BMTR$Date, format="%Y-%m-%d")
BMTR$Close <- as.numeric(BMTR$Close)## Warning: NAs introduced by coercionBMTR$Volume <- as.integer(BMTR$Volume)## Warning: NAs introduced by coercionstr(BMTR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "990.000000" "1040.000000" "1040.000000" "1020.000000" ...
## $ High : chr "1000.000000" "1050.000000" "1060.000000" "1040.000000" ...
## $ Low : chr "980.000000" "990.000000" "1030.000000" "1010.000000" ...
## $ Close : num 990 1040 1040 1020 1000 1010 1000 1010 1010 1010 ...
## $ Adj.Close: chr "916.497314" "962.785034" "962.785034" "944.269897" ...
## $ Volume : int 1876000 11375500 6734500 3374500 2079500 863000 943500 1249000 2476000 4 ...colSums(is.na(BMTR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bank Bumi Arta Tbk (BNBA)
BNBA <- read.csv(file="Data Saham/BNBA.JK.csv")
BNBA$Date <- as.Date(BNBA$Date, format="%Y-%m-%d")
BNBA$Close <- as.numeric(BNBA$Close)## Warning: NAs introduced by coercionBNBA$Volume <- as.integer(BNBA$Volume)## Warning: NAs introduced by coercionstr(BNBA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "155.000000" "155.000000" "146.000000" "146.000000" ...
## $ High : chr "155.000000" "155.000000" "155.000000" "147.000000" ...
## $ Low : chr "147.000000" "155.000000" "145.000000" "142.000000" ...
## $ Close : num 155 155 146 146 147 146 141 146 140 144 ...
## $ Adj.Close: chr "120.661812" "120.661812" "113.655647" "113.655647" ...
## $ Volume : int 13500 13500 996500 324500 385000 415500 176500 73500 314500 3 ...colSums(is.na(BNBA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# Bank CIMB Niaga (BNGA)
BNGA <- read.csv(file="Data Saham/BNGA.JK.csv")
BNGA$Date <- as.Date(BNGA$Date, format="%Y-%m-%d")
BNGA$Close <- as.numeric(BNGA$Close)## Warning: NAs introduced by coercionBNGA$Volume <- as.integer(BNGA$Volume)## Warning: NAs introduced by coercionstr(BNGA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1240.000000" "1270.000000" "1260.000000" "1260.000000" ...
## $ High : chr "1270.000000" "1280.000000" "1290.000000" "1260.000000" ...
## $ Low : chr "1230.000000" "1240.000000" "1250.000000" "1240.000000" ...
## $ Close : num 1240 1270 1260 1260 1260 1240 1250 1260 1240 1250 ...
## $ Adj.Close: chr "960.005798" "983.231628" "975.489685" "975.489685" ...
## $ Volume : int 190000 1097000 526000 239000 487500 1085500 907500 538000 425000 3 ...colSums(is.na(BNGA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bumi Serpong Damai Tbk (BSDE)
BSDE <- read.csv(file="Data Saham/BSDE.JK.csv")
BSDE$Date <- as.Date(BSDE$Date, format="%Y-%m-%d")
BSDE$Close <- as.numeric(BSDE$Close)## Warning: NAs introduced by coercionBSDE$Volume <- as.integer(BSDE$Volume)## Warning: NAs introduced by coercionstr(BSDE)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "950.000000" "980.000000" "1000.000000" "1000.000000" ...
## $ High : chr "980.000000" "990.000000" "1040.000000" "1020.000000" ...
## $ Low : chr "940.000000" "950.000000" "980.000000" "990.000000" ...
## $ Close : num 950 980 1000 1000 1010 1030 1050 1050 1060 1060 ...
## $ Adj.Close: chr "913.067505" "941.901367" "961.123779" "961.123779" ...
## $ Volume : int 6013000 8704000 48698000 48191500 46454500 16608000 12777500 9351500 10655500 0 ...colSums(is.na(BSDE))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Betonjaya Manunggal Tbk (BTON)
BTON <- read.csv(file="Data Saham/BTON.JK.csv")
BTON$Date <- as.Date(BTON$Date, format="%Y-%m-%d")
BTON$Close <- as.numeric(BTON$Close)## Warning: NAs introduced by coercionBTON$Volume <- as.integer(BTON$Volume)## Warning: NAs introduced by coercionstr(BTON)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "83.750000" "87.500000" "86.250000" "88.750000" ...
## $ High : chr "85.000000" "87.500000" "87.500000" "88.750000" ...
## $ Low : chr "83.750000" "85.000000" "86.250000" "87.500000" ...
## $ Close : num 83.8 87.5 86.2 88.8 88.8 ...
## $ Adj.Close: chr "79.111000" "82.653282" "81.472519" "83.834045" ...
## $ Volume : int 124000 1592000 256000 480000 480000 276000 14000 68000 372000 12 ...colSums(is.na(BTON))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Buana LMEDCs Lautan (BULL)
BULL <- read.csv(file="Data Saham/BULL.JK.csv")
BULL$Date <- as.Date(BULL$Date, format="%Y-%m-%d")
BULL$Close <- as.numeric(BULL$Close)
BULL$Volume <- as.integer(BULL$Volume)
str(BULL)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 653 666 666 660 660 ...
## $ High : num 666 666 666 666 660 ...
## $ Low : num 653 653 653 653 653 ...
## $ Close : num 653 666 666 660 660 ...
## $ Adj.Close: num 653 666 666 660 660 ...
## $ Volume : int 680419 2101887 2129776 1194257 469654 2718265 3859244 4586121 6310359 0 ...colSums(is.na(BULL))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Bumi Resources Tbk (BUMI)
BUMI <- read.csv(file="Data Saham/BUMI.JK.csv")
BUMI$Date <- as.Date(BUMI$Date, format="%Y-%m-%d")
BUMI$Close <- as.numeric(BUMI$Close)## Warning: NAs introduced by coercionBUMI$Volume <- as.integer(BUMI$Volume)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion to integer rangestr(BUMI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "2200.000000" "2275.000000" "2325.000000" "2350.000000" ...
## $ High : chr "2200.000000" "2275.000000" "2350.000000" "2400.000000" ...
## $ Low : chr "2150.000000" "2200.000000" "2275.000000" "2325.000000" ...
## $ Close : num 2200 2275 2325 2350 2325 ...
## $ Adj.Close: chr "2170.850098" "2244.856201" "2294.193848" "2318.862549" ...
## $ Volume : int 6712000 50775000 103023500 86186000 29003500 34552500 48847000 58508000 52333000 3 ...colSums(is.na(BUMI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 144# PT Cardig Aero Services Tbk (CASS)
CASS <- read.csv(file="Data Saham/CASS.JK.csv")
CASS$Date <- as.Date(CASS$Date, format="%Y-%m-%d")
CASS$Close <- as.numeric(CASS$Close)
CASS$Volume <- as.integer(CASS$Volume)
str(CASS)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 390 405 400 400 400 400 395 390 400 400 ...
## $ High : num 400 415 415 410 410 405 400 400 405 405 ...
## $ Low : num 390 390 400 400 400 395 390 390 400 395 ...
## $ Close : num 390 405 400 400 400 400 395 390 400 400 ...
## $ Adj.Close: num 353 367 363 363 363 ...
## $ Volume : int 48500 607500 199000 1067500 182000 157500 26000 59000 116500 0 ...colSums(is.na(CASS))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Wilmar Cahaya Indoensia Tbk (CEKA)
CEKA <- read.csv(file="Data Saham/CEKA.JK.csv")
CEKA$Date <- as.Date(CEKA$Date, format="%Y-%m-%d")
CEKA$Close <- as.numeric(CEKA$Close)## Warning: NAs introduced by coercionCEKA$Volume <- as.integer(CEKA$Volume)## Warning: NAs introduced by coercionstr(CEKA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "475.000000" "475.000000" "475.000000" "475.000000" ...
## $ High : chr "475.000000" "475.000000" "475.000000" "475.000000" ...
## $ Low : chr "475.000000" "475.000000" "475.000000" "475.000000" ...
## $ Close : num 475 475 475 475 475 475 475 475 475 390 ...
## $ Adj.Close: chr "328.530762" "328.530762" "328.530762" "328.530762" ...
## $ Volume : int 1000 1000 1000 1000 1000 1000 1000 1000 1000 0 ...colSums(is.na(CEKA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Centratama Telekomunikas Indonesia Tbk (CENT)
CENT <- read.csv(file="Data Saham/CENT.JK.csv")
CENT$Date <- as.Date(CENT$Date, format="%Y-%m-%d")
CENT$Close <- as.numeric(CENT$Close)## Warning: NAs introduced by coercionCENT$Volume <- as.integer(CENT$Volume)## Warning: NAs introduced by coercionstr(CENT)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "10.764376" "10.764376" "10.764376" "11.356911" ...
## $ High : chr "10.764376" "10.764376" "10.764376" "11.356911" ...
## $ Low : chr "10.764376" "10.764376" "10.764376" "10.863132" ...
## $ Close : num 10.8 10.8 10.8 11.4 11.4 ...
## $ Adj.Close: chr "10.764376" "10.764376" "10.764376" "11.356911" ...
## $ Volume : int 116448 116448 116448 10125 10125 10125 10125 10125 15188 0 ...colSums(is.na(CENT))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Citra Marga Nusaphala Persada (CMNP)
CMNP <- read.csv(file="Data Saham/CMNP.JK.csv")
CMNP$Date <- as.Date(CMNP$Date, format="%Y-%m-%d")
CMNP$Close <- as.numeric(CMNP$Close)## Warning: NAs introduced by coercionCMNP$Volume <- as.integer(CMNP$Volume)## Warning: NAs introduced by coercionstr(CMNP)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1099.636597" "1106.182007" "1099.636597" "1119.272949" ...
## $ High : chr "1106.182007" "1106.182007" "1112.727417" "1138.909302" ...
## $ Low : chr "1093.091064" "1086.545654" "1080.000244" "1099.636597" ...
## $ Close : num 1100 1106 1100 1119 1093 ...
## $ Adj.Close: chr "1085.358398" "1091.818726" "1085.358398" "1104.739868" ...
## $ Volume : int 2491805 4177707 10615761 16736802 3131943 1808888 2243541 2598749 487361 0 ...colSums(is.na(CMNP))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Charoen Pokphand Indonesia Tbk (CPIN)
CPIN <- read.csv(file="Data Saham/CPIN.JK.csv")
CPIN$Date <- as.Date(CPIN$Date, format="%Y-%m-%d")
CPIN$Close <- as.numeric(CPIN$Close)## Warning: NAs introduced by coercionCPIN$Volume <- as.integer(CPIN$Volume)## Warning: NAs introduced by coercionstr(CPIN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "2100.000000" "2175.000000" "2200.000000" "2225.000000" ...
## $ High : chr "2150.000000" "2200.000000" "2250.000000" "2250.000000" ...
## $ Low : chr "2100.000000" "2125.000000" "2175.000000" "2175.000000" ...
## $ Close : num 2100 2175 2200 2225 2225 ...
## $ Adj.Close: chr "1789.970215" "1853.897705" "1875.207275" "1896.516235" ...
## $ Volume : int 4532500 10980000 26533000 19671000 7765500 13601000 18255000 32687500 6390500 3 ...colSums(is.na(CPIN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Ciputra Development Tbk (CTRA)
CTRA <- read.csv(file="Data Saham/CTRA.JK.csv")
CTRA$Date <- as.Date(CTRA$Date, format="%Y-%m-%d")
CTRA$Close <- as.numeric(CTRA$Close)## Warning: NAs introduced by coercionCTRA$Volume <- as.integer(CTRA$Volume)## Warning: NAs introduced by coercionstr(CTRA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "530.915955" "540.747742" "560.411316" "550.579529" ...
## $ High : chr "550.579529" "540.747742" "589.906616" "570.243103" ...
## $ Low : chr "521.084167" "530.915955" "550.579529" "540.747742" ...
## $ Close : num 531 541 560 551 560 ...
## $ Adj.Close: chr "472.891052" "481.648254" "499.162659" "490.405457" ...
## $ Volume : int 2035745 6109271 13340924 14316841 7484404 10793572 7215379 12160568 1959971 3 ...colSums(is.na(CTRA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Duta Anggada Realty Tbk (DART)
DART <- read.csv(file="Data Saham/DART.JK.csv")
DART$Date <- as.Date(DART$Date, format="%Y-%m-%d")
DART$Close <- as.numeric(DART$Close)
DART$Volume <- as.integer(DART$Volume)
str(DART)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 520 495 510 485 500 500 495 495 485 485 ...
## $ High : num 530 540 550 520 520 510 510 500 490 485 ...
## $ Low : num 435 470 485 475 485 495 490 490 470 485 ...
## $ Close : num 520 495 510 485 500 500 495 495 485 485 ...
## $ Adj.Close: num 423 402 415 394 407 ...
## $ Volume : int 19242500 12601000 12025500 2946500 2603000 2128000 1554500 1632500 641500 0 ...colSums(is.na(DART))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Nusa Konstruksi Enjiniring Tbk (DGIK)
DGIK <- read.csv(file="Data Saham/DGIK.JK.csv")
DGIK$Date <- as.Date(DGIK$Date, format="%Y-%m-%d")
DGIK$Close <- as.numeric(DGIK$Close)## Warning: NAs introduced by coercionDGIK$Volume <- as.integer(DGIK$Volume)## Warning: NAs introduced by coercionstr(DGIK)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "90.000000" "91.000000" "90.000000" "92.000000" ...
## $ High : chr "91.000000" "92.000000" "92.000000" "94.000000" ...
## $ Low : chr "88.000000" "90.000000" "90.000000" "91.000000" ...
## $ Close : num 90 91 90 92 91 91 91 89 92 92 ...
## $ Adj.Close: chr "85.116470" "86.062210" "85.116470" "87.007942" ...
## $ Volume : int 1043000 3335000 2439500 13558000 1359000 166000 561000 192000 3654500 0 ...colSums(is.na(DGIK))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Delta Dunia Makmur Tbk (DOID)
DOID <- read.csv(file="Data Saham/DOID.JK.csv")
DOID$Date <- as.Date(DOID$Date, format="%Y-%m-%d")
DOID$Close <- as.numeric(DOID$Close)## Warning: NAs introduced by coercionDOID$Volume <- as.integer(DOID$Volume)## Warning: NAs introduced by coercionstr(DOID)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "670.000000" "670.000000" "670.000000" "670.000000" ...
## $ High : chr "670.000000" "690.000000" "680.000000" "680.000000" ...
## $ Low : chr "660.000000" "660.000000" "660.000000" "660.000000" ...
## $ Close : num 670 670 670 670 670 670 670 660 660 670 ...
## $ Adj.Close: chr "670.000000" "670.000000" "670.000000" "670.000000" ...
## $ Volume : int 1082500 14249000 4877500 8583000 15508000 6860000 5417500 16060000 15511000 3 ...colSums(is.na(DOID))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Darya-Varia Laboratoria (DVLA)
DVLA <- read.csv(file="Data Saham/DVLA.JK.csv")
DVLA$Date <- as.Date(DVLA$Date, format="%Y-%m-%d")
DVLA$Close <- as.numeric(DVLA$Close)## Warning: NAs introduced by coercionDVLA$Volume <- as.integer(DVLA$Volume)## Warning: NAs introduced by coercionstr(DVLA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1150.000000" "1150.000000" "1150.000000" "1150.000000" ...
## $ High : chr "1150.000000" "1150.000000" "1150.000000" "1150.000000" ...
## $ Low : chr "1150.000000" "1150.000000" "1150.000000" "1150.000000" ...
## $ Close : num 1150 1150 1150 1150 1150 1150 1150 1150 1200 1210 ...
## $ Adj.Close: chr "785.756042" "785.756042" "785.756042" "785.756042" ...
## $ Volume : int 75000 75000 75000 75000 75000 75000 75000 75000 56000 3 ...colSums(is.na(DVLA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Elnusa Tbk (ELSA)
ELSA <- read.csv(file="Data Saham/ELSA.JK.csv")
ELSA$Date <- as.Date(ELSA$Date, format="%Y-%m-%d")
ELSA$Close <- as.numeric(ELSA$Close)## Warning: NAs introduced by coercionELSA$Volume <- as.integer(ELSA$Volume)## Warning: NAs introduced by coercionstr(ELSA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "230.000000" "240.000000" "240.000000" "230.000000" ...
## $ High : chr "240.000000" "240.000000" "245.000000" "240.000000" ...
## $ Low : chr "230.000000" "230.000000" "235.000000" "230.000000" ...
## $ Close : num 230 240 240 230 235 235 240 240 240 255 ...
## $ Adj.Close: chr "172.414093" "179.910370" "179.910370" "172.414093" ...
## $ Volume : int 5577000 4940500 9823500 7615000 2255000 2739500 3847000 18093000 23676500 1 ...colSums(is.na(ELSA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Elang Mahkota Teknologi (EMTK)
EMTK <- read.csv(file="Data Saham/EMTK.JK.csv")
EMTK$Date <- as.Date(EMTK$Date, format="%Y-%m-%d")
EMTK$Close <- as.numeric(EMTK$Close)
EMTK$Volume <- as.integer(EMTK$Volume)
str(EMTK)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 330 330 302 330 315 ...
## $ High : num 330 330 332 335 315 ...
## $ Low : num 330 330 300 330 315 ...
## $ Close : num 330 330 302 330 315 ...
## $ Adj.Close: num 197 197 180 197 188 ...
## $ Volume : int 20000 35000 100000 30000 5000 5000 40000 15000 15000 30 ...colSums(is.na(EMTK))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Erajaya Swasembada Tbk (ERAA)
ERAA <- read.csv(file="Data Saham/ERAA.JK.csv")
ERAA$Date <- as.Date(ERAA$Date, format="%Y-%m-%d")
ERAA$Close <- as.numeric(ERAA$Close)
ERAA$Volume <- as.integer(ERAA$Volume)
str(ERAA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 198 196 192 192 192 196 198 198 198 200 ...
## $ High : num 200 196 194 194 194 196 202 200 198 200 ...
## $ Low : num 196 192 192 190 192 192 196 196 196 196 ...
## $ Close : num 198 196 192 192 192 196 198 198 198 200 ...
## $ Adj.Close: num 152 150 147 147 147 ...
## $ Volume : int 27500 44217500 21090000 16970000 66795000 24260000 40732500 6602500 15005000 28305000 ...colSums(is.na(ERAA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT XL Axiata Tbk (EXCL)
EXCL <- read.csv(file="Data Saham/EXCL.JK.csv")
EXCL$Date <- as.Date(EXCL$Date, format="%Y-%m-%d")
EXCL$Close <- as.numeric(EXCL$Close)## Warning: NAs introduced by coercionEXCL$Volume <- as.integer(EXCL$Volume)## Warning: NAs introduced by coercionstr(EXCL)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "4414.098633" "4438.758301" "4463.418457" "4512.737793" ...
## $ High : chr "4463.418457" "4463.418457" "4512.737793" "4512.737793" ...
## $ Low : chr "4389.438965" "4414.098633" "4414.098633" "4463.418457" ...
## $ Close : num 4414 4439 4463 4513 4488 ...
## $ Adj.Close: chr "3964.256836" "3986.403564" "4008.550293" "4052.843994" ...
## $ Volume : int 722837 2328184 3904639 3971549 2390533 3672479 11625717 3224888 19657016 3 ...colSums(is.na(EXCL))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Lotte Chemical Titan Tbk (FPNI)
FPNI <- read.csv(file="Data Saham/FPNI.JK.csv")
FPNI$Date <- as.Date(FPNI$Date, format="%Y-%m-%d")
FPNI$Close <- as.numeric(FPNI$Close)## Warning: NAs introduced by coercionFPNI$Volume <- as.integer(FPNI$Volume)## Warning: NAs introduced by coercionstr(FPNI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "157.000000" "158.000000" "158.000000" "147.000000" ...
## $ High : chr "157.000000" "159.000000" "158.000000" "159.000000" ...
## $ Low : chr "157.000000" "156.000000" "158.000000" "146.000000" ...
## $ Close : num 157 158 158 147 144 147 143 145 145 145 ...
## $ Adj.Close: chr "157.000000" "158.000000" "158.000000" "147.000000" ...
## $ Volume : int 4000 183000 183000 2191000 169500 236500 196000 215000 766000 3 ...colSums(is.na(FPNI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Smartfren Telecom Tbk (FREN)
FREN <- read.csv(file="Data Saham/FREN.JK.csv")
FREN$Date <- as.Date(FREN$Date, format="%Y-%m-%d")
FREN$Close <- as.numeric(FREN$Close)## Warning: NAs introduced by coercionFREN$Volume <- as.integer(FREN$Volume)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion to integer rangestr(FREN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "996.099304" "996.099304" "996.099304" "996.099304" ...
## $ High : chr "996.099304" "996.099304" "996.099304" "996.099304" ...
## $ Low : chr "996.099304" "996.099304" "996.099304" "996.099304" ...
## $ Close : num 996 996 996 996 996 ...
## $ Adj.Close: chr "996.099304" "996.099304" "996.099304" "996.099304" ...
## $ Volume : int 3287 3287 426 426 150 150 5847 15058 1606 0 ...colSums(is.na(FREN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 64# PT Harum Energy Tbk (HRUM)
HRUM <- read.csv(file="Data Saham/HRUM.JK.csv")
HRUM$Date <- as.Date(HRUM$Date, format="%Y-%m-%d")
HRUM$Close <- as.numeric(HRUM$Close)
HRUM$Volume <- as.integer(HRUM$Volume)
str(HRUM)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 1370 1430 1450 1430 1400 1440 1480 1460 1440 1460 ...
## $ High : num 1370 1430 1480 1460 1420 1440 1480 1490 1470 1470 ...
## $ Low : num 1350 1370 1440 1420 1390 1380 1440 1450 1440 1450 ...
## $ Close : num 1370 1430 1450 1430 1400 1440 1480 1460 1440 1460 ...
## $ Adj.Close: num 971 1013 1027 1013 992 ...
## $ Volume : int 7555000 58255000 82810000 29797500 19372500 20675000 53637500 31047500 20452500 15 ...colSums(is.na(HRUM))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Indofood CBP Sukses Makmur (ICBP)
ICBP <- read.csv(file="Data Saham/ICBP.JK.csv")
ICBP$Date <- as.Date(ICBP$Date, format="%Y-%m-%d")
ICBP$Close <- as.numeric(ICBP$Close)## Warning: NAs introduced by coercionICBP$Volume <- as.integer(ICBP$Volume)## Warning: NAs introduced by coercionstr(ICBP)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "2575.000000" "2625.000000" "2650.000000" "2575.000000" ...
## $ High : chr "2575.000000" "2625.000000" "2675.000000" "2650.000000" ...
## $ Low : chr "2500.000000" "2550.000000" "2600.000000" "2575.000000" ...
## $ Close : num 2575 2625 2650 2575 2525 ...
## $ Adj.Close: chr "2018.937012" "2058.139404" "2077.740967" "2018.937012" ...
## $ Volume : int 1490000 3474000 1452000 3729000 5093000 4320000 2218000 3678000 4066000 6 ...colSums(is.na(ICBP))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Indofarma Tbk (INAF)
INAF <- read.csv(file="Data Saham/INAF.JK.csv")
INAF$Date <- as.Date(INAF$Date, format="%Y-%m-%d")
INAF$Close <- as.numeric(INAF$Close)## Warning: NAs introduced by coercionINAF$Volume <- as.integer(INAF$Volume)## Warning: NAs introduced by coercionstr(INAF)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "161.000000" "165.000000" "165.000000" "163.000000" ...
## $ High : chr "166.000000" "168.000000" "171.000000" "167.000000" ...
## $ Low : chr "158.000000" "160.000000" "164.000000" "162.000000" ...
## $ Close : num 161 165 165 163 175 177 174 169 171 172 ...
## $ Adj.Close: chr "160.212250" "164.192673" "164.192673" "162.202454" ...
## $ Volume : int 19036000 25929500 43909500 7241000 116148500 136605000 17428000 17728000 5979500 3 ...colSums(is.na(INAF))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Vale Indonesia Tbk (INCO)
INCO <- read.csv(file="Data Saham/INCO.JK.csv")
INCO$Date <- as.Date(INCO$Date, format="%Y-%m-%d")
INCO$Close <- as.numeric(INCO$Close)## Warning: NAs introduced by coercionINCO$Volume <- as.integer(INCO$Volume)## Warning: NAs introduced by coercionstr(INCO)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "3200.000000" "3250.000000" "3400.000000" "3400.000000" ...
## $ High : chr "3200.000000" "3250.000000" "3400.000000" "3450.000000" ...
## $ Low : chr "3150.000000" "3200.000000" "3275.000000" "3375.000000" ...
## $ Close : num 3200 3250 3400 3400 3375 ...
## $ Adj.Close: chr "2883.845459" "2928.905518" "3064.085938" "3064.085938" ...
## $ Volume : int 624000 1515000 8750000 4571000 1588500 1796000 3296500 5393000 3075000 0 ...colSums(is.na(INCO))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Indo-Rama Sythetics Tbk (INDR)
INDR <- read.csv(file="Data Saham/INDR.JK.csv")
INDR$Date <- as.Date(INDR$Date, format="%Y-%m-%d")
INDR$Close <- as.numeric(INDR$Close)## Warning: NAs introduced by coercionINDR$Volume <- as.integer(INDR$Volume)## Warning: NAs introduced by coercionstr(INDR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1980.000000" "1980.000000" "1950.000000" "1920.000000" ...
## $ High : chr "1980.000000" "1980.000000" "1950.000000" "1930.000000" ...
## $ Low : chr "1980.000000" "1980.000000" "1900.000000" "1920.000000" ...
## $ Close : num 1980 1980 1950 1920 1920 ...
## $ Adj.Close: chr "1653.481079" "1653.481079" "1628.428223" "1603.375488" ...
## $ Volume : int 1000 1000 27500 7000 7000 11000 7000 6500 128000 3 ...colSums(is.na(INDR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Indika Energy Tbk (INDY)
INDY <- read.csv(file="Data Saham/INDY.JK.csv")
INDY$Date <- as.Date(INDY$Date, format="%Y-%m-%d")
INDY$Close <- as.numeric(INDY$Close)## Warning: NAs introduced by coercionINDY$Volume <- as.integer(INDY$Volume)## Warning: NAs introduced by coercionstr(INDY)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "2125.000000" "2200.000000" "2275.000000" "2250.000000" ...
## $ High : chr "2175.000000" "2225.000000" "2300.000000" "2300.000000" ...
## $ Low : chr "2125.000000" "2150.000000" "2200.000000" "2225.000000" ...
## $ Close : num 2125 2200 2275 2250 2200 ...
## $ Adj.Close: chr "1469.005859" "1520.853027" "1572.700317" "1555.417969" ...
## $ Volume : int 3167500 8377000 20245500 5713500 3960500 5065500 34898500 18766500 10172000 1 ...colSums(is.na(INDY))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Indah Kiat Pulp & Paper Tbk (INKP)
INKP <- read.csv(file="Data Saham/INKP.JK.csv")
INKP$Date <- as.Date(INKP$Date, format="%Y-%m-%d")
INKP$Close <- as.numeric(INKP$Close)## Warning: NAs introduced by coercionINKP$Volume <- as.integer(INKP$Volume)## Warning: NAs introduced by coercionstr(INKP)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1220.000000" "1240.000000" "1250.000000" "1220.000000" ...
## $ High : chr "1240.000000" "1240.000000" "1250.000000" "1250.000000" ...
## $ Low : chr "1210.000000" "1220.000000" "1230.000000" "1220.000000" ...
## $ Close : num 1220 1240 1250 1220 1250 1270 1250 1270 1270 1280 ...
## $ Adj.Close: chr "1073.112427" "1090.704468" "1099.500244" "1073.112427" ...
## $ Volume : int 266000 469000 814000 804500 2941000 853000 2303000 1110500 447000 3 ...colSums(is.na(INKP))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Intraco Penta Tbk (INTA)
INTA <- read.csv(file="Data Saham/INTA.JK.csv")
INTA$Date <- as.Date(INTA$Date, format="%Y-%m-%d")
INTA$Close <- as.numeric(INTA$Close)## Warning: NAs introduced by coercionINTA$Volume <- as.integer(INTA$Volume)## Warning: NAs introduced by coercionstr(INTA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "505.384460" "513.807495" "522.230591" "505.384460" ...
## $ High : chr "505.384460" "522.230591" "522.230591" "522.230591" ...
## $ Low : chr "496.961365" "496.961365" "513.807495" "505.384460" ...
## $ Close : num 505 514 522 505 522 ...
## $ Adj.Close: chr "489.591187" "497.751007" "505.910889" "489.591187" ...
## $ Volume : int 3343197 15000461 10674250 6919089 31366220 19378910 14567721 47013714 166782931 2 ...colSums(is.na(INTA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Jembo Cable Company Tbk (JECC)
JECC <- read.csv(file="Data Saham/JECC.JK.csv")
JECC$Date <- as.Date(JECC$Date, format="%Y-%m-%d")
JECC$Close <- as.numeric(JECC$Close)
JECC$Volume <- as.integer(JECC$Volume)
str(JECC)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 600 590 590 590 600 650 670 670 680 680 ...
## $ High : num 600 600 590 600 610 650 740 680 710 690 ...
## $ Low : num 600 590 570 590 600 600 650 670 650 670 ...
## $ Close : num 600 590 590 590 600 650 670 670 680 680 ...
## $ Adj.Close: num 425 418 418 418 425 ...
## $ Volume : int 5000 19500 34500 107500 85000 310000 4555500 246000 1669500 4 ...colSums(is.na(JECC))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Jaya Real Property Tbk (JRPT)
JRPT <- read.csv(file="Data Saham/JRPT.JK.csv")
JRPT$Date <- as.Date(JRPT$Date, format="%Y-%m-%d")
JRPT$Close <- as.numeric(JRPT$Close)## Warning: NAs introduced by coercionJRPT$Volume <- as.integer(JRPT$Volume)## Warning: NAs introduced by coercionstr(JRPT)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "440.000000" "440.000000" "440.000000" "440.000000" ...
## $ High : chr "440.000000" "440.000000" "440.000000" "440.000000" ...
## $ Low : chr "440.000000" "440.000000" "440.000000" "440.000000" ...
## $ Close : num 440 440 440 440 440 440 440 440 440 440 ...
## $ Adj.Close: chr "303.625366" "303.625366" "303.625366" "303.625366" ...
## $ Volume : int 707500 707500 707500 707500 707500 707500 707500 707500 707500 0 ...colSums(is.na(JRPT))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Jasa Marga (Persero) Tbk (JSMR)
JSMR <- read.csv(file="Data Saham/JSMR.JK.csv")
JSMR$Date <- as.Date(JSMR$Date, format="%Y-%m-%d")
JSMR$Close <- as.numeric(JSMR$Close)## Warning: NAs introduced by coercionJSMR$Volume <- as.integer(JSMR$Volume)## Warning: NAs introduced by coercionstr(JSMR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "4190.186523" "4165.245117" "4364.777832" "4439.602539" ...
## $ High : chr "4190.186523" "4215.127930" "4389.719238" "4564.310547" ...
## $ Low : chr "4115.361816" "4165.245117" "4190.186523" "4389.719238" ...
## $ Close : num 4190 4165 4365 4440 4290 ...
## $ Adj.Close: chr "3730.042480" "3707.839600" "3885.461182" "3952.068604" ...
## $ Volume : int 964253 1109091 17623177 14834160 15798914 8741925 9555827 4737569 3945218 0 ...colSums(is.na(JSMR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Kimia Farma Tbk (KAEF)
KAEF <- read.csv(file="Data Saham/KAEF.JK.csv")
KAEF$Date <- as.Date(KAEF$Date, format="%Y-%m-%d")
KAEF$Close <- as.numeric(KAEF$Close)## Warning: NAs introduced by coercionKAEF$Volume <- as.integer(KAEF$Volume)## Warning: NAs introduced by coercionstr(KAEF)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "330.000000" "340.000000" "340.000000" "335.000000" ...
## $ High : chr "335.000000" "340.000000" "345.000000" "340.000000" ...
## $ Low : chr "320.000000" "330.000000" "335.000000" "330.000000" ...
## $ Close : num 330 340 340 335 340 340 330 330 330 335 ...
## $ Adj.Close: chr "303.828918" "313.035828" "313.035828" "308.432404" ...
## $ Volume : int 1877000 3186000 5308000 1828000 5628500 4791500 1977000 1581000 662500 3 ...colSums(is.na(KAEF))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT KMI Wire and Cable Tbk (KBLI)
KBLI <- read.csv(file="Data Saham/KBLI.JK.csv")
KBLI$Date <- as.Date(KBLI$Date, format="%Y-%m-%d")
KBLI$Close <- as.numeric(KBLI$Close)
KBLI$Volume <- as.integer(KBLI$Volume)
str(KBLI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 103 106 105 114 112 111 110 111 109 109 ...
## $ High : num 104 106 109 123 115 111 114 114 112 109 ...
## $ Low : num 103 103 103 107 111 109 110 110 108 109 ...
## $ Close : num 103 106 105 114 112 111 110 111 109 109 ...
## $ Adj.Close: num 86.4 89 88.1 95.7 94 ...
## $ Volume : int 316500 110500 687000 19473000 681500 617000 1727500 1200000 217000 0 ...colSums(is.na(KBLI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Kalbe Farma Tbk (KLBF)
KLBF <- read.csv(file="Data Saham/KLBF.JK.csv")
KLBF$Date <- as.Date(KLBF$Date, format="%Y-%m-%d")
KLBF$Close <- as.numeric(KLBF$Close)
KLBF$Volume <- as.integer(KLBF$Volume)
str(KLBF)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 685 695 690 690 690 700 700 695 695 690 ...
## $ High : num 685 695 700 700 695 700 705 705 700 700 ...
## $ Low : num 675 680 690 685 685 685 690 680 680 685 ...
## $ Close : num 685 695 690 690 690 700 700 695 695 690 ...
## $ Adj.Close: num 561 569 565 565 565 ...
## $ Volume : int 18735000 34187500 23417500 15250000 18040000 20852500 12980000 22215000 14732500 15 ...colSums(is.na(KLBF))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Kokoh Inti Arebama Tbk (KOIN)
KOIN <- read.csv(file="Data Saham/KOIN.JK.csv")
KOIN$Date <- as.Date(KOIN$Date, format="%Y-%m-%d")
KOIN$Close <- as.numeric(KOIN$Close)## Warning: NAs introduced by coercionKOIN$Volume <- as.integer(KOIN$Volume)## Warning: NAs introduced by coercionstr(KOIN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "184.000000" "189.000000" "183.000000" "186.000000" ...
## $ High : chr "195.000000" "189.000000" "183.000000" "186.000000" ...
## $ Low : chr "184.000000" "188.000000" "183.000000" "182.000000" ...
## $ Close : num 184 189 183 186 181 183 188 181 181 186 ...
## $ Adj.Close: chr "184.000000" "189.000000" "183.000000" "186.000000" ...
## $ Volume : int 54000 10500 5000 8000 6000 23500 5500 11000 5500 3 ...colSums(is.na(KOIN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT MNC Land Tbk (KPIG)
KPIG <- read.csv(file="Data Saham/KPIG.JK.csv")
KPIG$Date <- as.Date(KPIG$Date, format="%Y-%m-%d")
KPIG$Close <- as.numeric(KPIG$Close)## Warning: NAs introduced by coercionKPIG$Volume <- as.integer(KPIG$Volume)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion to integer rangestr(KPIG)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "67.000000" "67.000000" "67.000000" "68.000000" ...
## $ High : chr "67.000000" "67.000000" "69.000000" "69.000000" ...
## $ Low : chr "63.000000" "65.000000" "67.000000" "68.000000" ...
## $ Close : num 67 67 67 68 67 68 69 67 67 67 ...
## $ Adj.Close: chr "65.622780" "65.622780" "65.622780" "66.602226" ...
## $ Volume : int 445000 645000 175000 40000 450000 1115000 550000 995000 75000 30 ...colSums(is.na(KPIG))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 3# PT Langgeng Makmur Industri Tbk (LMPI)
LMPI <- read.csv(file="Data Saham/LMPI.JK.csv")
LMPI$Date <- as.Date(LMPI$Date, format="%Y-%m-%d")
LMPI$Close <- as.numeric(LMPI$Close)
LMPI$Volume <- as.integer(LMPI$Volume)
str(LMPI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 210 215 220 220 215 215 220 225 225 230 ...
## $ High : num 210 215 225 220 220 215 220 230 230 230 ...
## $ Low : num 210 205 210 220 215 210 205 215 220 225 ...
## $ Close : num 210 215 220 220 215 215 220 225 225 230 ...
## $ Adj.Close: num 210 215 220 220 215 215 220 225 225 230 ...
## $ Volume : int 500 38500 696000 500 1500 61500 199000 788000 988500 3 ...colSums(is.na(LMPI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Medco Energi Internasional Tbk (MEDC)
MEDC <- read.csv(file="Data Saham/MEDC.JK.csv")
MEDC$Date <- as.Date(MEDC$Date, format="%Y-%m-%d")
MEDC$Close <- as.numeric(MEDC$Close)
MEDC$Volume <- as.integer(MEDC$Volume)
str(MEDC)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 457 457 467 457 452 ...
## $ High : num 462 467 476 471 462 ...
## $ Low : num 457 448 457 448 452 ...
## $ Close : num 457 457 467 457 452 ...
## $ Adj.Close: num 431 431 440 431 426 ...
## $ Volume : int 790124 9704624 13678873 17440498 3407249 5659499 13384873 7943249 7271249 10 ...colSums(is.na(MEDC))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Merck Tbk (MERK)
MERK <- read.csv(file="Data Saham/MERK.JK.csv")
MERK$Date <- as.Date(MERK$Date, format="%Y-%m-%d")
MERK$Close <- as.numeric(MERK$Close)## Warning: NAs introduced by coercionMERK$Volume <- as.integer(MERK$Volume)## Warning: NAs introduced by coercionstr(MERK)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "6625.000000" "6625.000000" "6625.000000" "6625.000000" ...
## $ High : chr "6625.000000" "6625.000000" "6625.000000" "6625.000000" ...
## $ Low : chr "6625.000000" "6625.000000" "6625.000000" "6625.000000" ...
## $ Close : num 6625 6625 6625 6625 6625 ...
## $ Adj.Close: chr "2872.202148" "2872.202148" "2872.202148" "2872.202148" ...
## $ Volume : int 30000 30000 30000 30000 30000 30000 40000 40000 40000 0 ...colSums(is.na(MERK))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 2 0 2# PT Nusantara Infrastructure (META)
META <- read.csv(file="Data Saham/META.JK.csv")
META$Date <- as.Date(META$Date, format="%Y-%m-%d")
META$Close <- as.numeric(META$Close)## Warning: NAs introduced by coercionMETA$Volume <- as.integer(META$Volume)## Warning: NAs introduced by coercionstr(META)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "202.581985" "202.581985" "202.581985" "196.652756" ...
## $ High : chr "202.581985" "202.581985" "202.581985" "202.581985" ...
## $ Low : chr "193.688141" "197.640961" "197.640961" "196.652756" ...
## $ Close : num 203 203 203 197 198 ...
## $ Adj.Close: chr "188.597427" "188.597427" "188.597427" "183.077484" ...
## $ Volume : int 3578711 1805799 919849 1181435 1235067 3056046 2588026 2799520 19365419 3 ...colSums(is.na(META))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Multi BMEDCng Indonesia (MLBI)
MLBI <- read.csv(file="Data Saham/MLBI.JK.csv")
MLBI$Date <- as.Date(MLBI$Date, format="%Y-%m-%d")
MLBI$Close <- as.numeric(MLBI$Close)## Warning: NAs introduced by coercionMLBI$Volume <- as.integer(MLBI$Volume)## Warning: NAs introduced by coercionstr(MLBI)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "3590.000000" "3590.000000" "3590.000000" "3590.000000" ...
## $ High : chr "3590.000000" "3590.000000" "3590.000000" "3590.000000" ...
## $ Low : chr "3590.000000" "3590.000000" "3590.000000" "3590.000000" ...
## $ Close : num 3590 3590 3590 3590 3590 3590 3750 3840 3840 3840 ...
## $ Adj.Close: chr "2447.634766" "2447.634766" "2447.634766" "2447.634766" ...
## $ Volume : int 200000 200000 200000 200000 200000 200000 50000 100000 100000 0 ...colSums(is.na(MLBI))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 2 0 2# PT Mulia Industrindo (MLIA)
MLIA <- read.csv(file="Data Saham/MLIA.JK.csv")
MLIA$Date <- as.Date(MLIA$Date, format="%Y-%m-%d")
MLIA$Close <- as.numeric(MLIA$Close)
MLIA$Volume <- as.integer(MLIA$Volume)
str(MLIA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 90 90 86 86 86 86 82 88 86 84 ...
## $ High : num 90 90 87 87 86 86 86 88 86 88 ...
## $ Low : num 90 90 81 86 86 85 82 88 82 84 ...
## $ Close : num 90 90 86 86 86 86 82 88 86 84 ...
## $ Adj.Close: num 90 90 86 86 86 86 82 88 86 84 ...
## $ Volume : int 5000 5000 505000 880000 1190000 5530000 622500 2500 2497500 15 ...colSums(is.na(MLIA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Metrodata Electronics Tbk (MTDL)
MTDL <- read.csv(file="Data Saham/MTDL.JK.csv")
MTDL$Date <- as.Date(MTDL$Date, format="%Y-%m-%d")
MTDL$Close <- as.numeric(MTDL$Close)
MTDL$Volume <- as.integer(MTDL$Volume)
str(MTDL)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 21.2 21.6 21.4 21.6 21.4 ...
## $ High : num 21.8 21.6 21.8 21.8 22 ...
## $ Low : num 21.2 21.4 21.4 21.6 21.4 ...
## $ Close : num 21.2 21.6 21.4 21.6 21.4 ...
## $ Adj.Close: num 17.3 17.6 17.5 17.6 17.5 ...
## $ Volume : int 1915873 729726 800786 2249306 2790452 194047 4823847 6641331 2722125 16 ...colSums(is.na(MTDL))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Samindo Resources Tbk (MYOH)
MYOH <- read.csv(file="Data Saham/MYOH.JK.csv")
MYOH$Date <- as.Date(MYOH$Date, format="%Y-%m-%d")
MYOH$Close <- as.numeric(MYOH$Close)## Warning: NAs introduced by coercionMYOH$Volume <- as.integer(MYOH$Volume)## Warning: NAs introduced by coercionstr(MYOH)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1349.265625" "1349.265625" "1349.265625" "1349.265625" ...
## $ High : chr "1349.265625" "1349.265625" "1349.265625" "1349.265625" ...
## $ Low : chr "1349.265625" "1349.265625" "1349.265625" "1349.265625" ...
## $ Close : num 1349 1349 1349 1349 1349 ...
## $ Adj.Close: chr "653.340210" "653.340210" "653.340210" "653.340210" ...
## $ Volume : int 7164971 7164971 7164971 7164971 7164971 7164971 2777392 7474028 6746714 2 ...colSums(is.na(MYOH))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Panorama Sentrawisata (PANR)
PANR <- read.csv(file="Data Saham/PANR.JK (1).csv")
PANR$Date <- as.Date(PANR$Date, format="%Y-%m-%d")
PANR$Close <- as.numeric(PANR$Close)## Warning: NAs introduced by coercionPANR$Volume <- as.integer(PANR$Volume)## Warning: NAs introduced by coercionstr(PANR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "156.000000" "156.000000" "155.000000" "156.000000" ...
## $ High : chr "156.000000" "156.000000" "157.000000" "156.000000" ...
## $ Low : chr "155.000000" "156.000000" "155.000000" "155.000000" ...
## $ Close : num 156 156 155 156 156 157 155 157 156 156 ...
## $ Adj.Close: chr "143.328308" "143.328308" "142.409546" "143.328308" ...
## $ Volume : int 56500 59000 69000 55000 70000 146000 190500 68000 129000 0 ...colSums(is.na(PANR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Perusahaan Gas Negara (PGAS)
PGAS <- read.csv(file="Data Saham/PGAS.JK.csv")
PGAS$Date <- as.Date(PGAS$Date, format="%Y-%m-%d")
PGAS$Close <- as.numeric(PGAS$Close)## Warning: NAs introduced by coercionPGAS$Volume <- as.integer(PGAS$Volume)## Warning: NAs introduced by coercionstr(PGAS)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "3075.000000" "3175.000000" "3200.000000" "3225.000000" ...
## $ High : chr "3175.000000" "3200.000000" "3225.000000" "3275.000000" ...
## $ Low : chr "3050.000000" "3100.000000" "3175.000000" "3175.000000" ...
## $ Close : num 3075 3175 3200 3225 3150 ...
## $ Adj.Close: chr "1946.767456" "2010.076782" "2025.904541" "2041.731567" ...
## $ Volume : int 10479000 11045000 17310500 23366000 19565000 8642500 21266000 16416000 10822500 4 ...colSums(is.na(PGAS))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Pelangi Indah Canindo Tbk (PICO)
PICO <- read.csv(file="Data Saham/PICO.JK.csv")
PICO$Date <- as.Date(PICO$Date, format="%Y-%m-%d")
PICO$Close <- as.numeric(PICO$Close)## Warning: NAs introduced by coercionPICO$Volume <- as.integer(PICO$Volume)## Warning: NAs introduced by coercionstr(PICO)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "193.000000" "193.000000" "194.000000" "193.000000" ...
## $ High : chr "193.000000" "193.000000" "194.000000" "193.000000" ...
## $ Low : chr "193.000000" "193.000000" "193.000000" "193.000000" ...
## $ Close : num 193 193 194 193 205 205 205 230 210 200 ...
## $ Adj.Close: chr "184.163177" "184.163177" "185.117386" "184.163177" ...
## $ Volume : int 25500 50000 1500 25000 1647500 23000 12500 15864500 7354000 3 ...colSums(is.na(PICO))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Pembangunan Jaya Ancol (PJAA)
PJAA <- read.csv(file="Data Saham/PJAA.JK (1).csv")
PJAA$Date <- as.Date(PJAA$Date, format="%Y-%m-%d")
PJAA$Close <- as.numeric(PJAA$Close)## Warning: NAs introduced by coercionPJAA$Volume <- as.integer(PJAA$Volume)## Warning: NAs introduced by coercionstr(PJAA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "950.000000" "1020.000000" "1050.000000" "1050.000000" ...
## $ High : chr "950.000000" "1020.000000" "1050.000000" "1050.000000" ...
## $ Low : chr "950.000000" "1000.000000" "1050.000000" "1050.000000" ...
## $ Close : num 950 1020 1050 1050 1050 1050 930 940 990 1000 ...
## $ Adj.Close: chr "708.729431" "760.951599" "783.332397" "783.332397" ...
## $ Volume : int 500 1000 500 500 500 500 9500 36000 7500 3 ...colSums(is.na(PJAA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Bank Pan Indonesia Tbk (PNBN)
PNBN <- read.csv(file="Data Saham/PNBN.JK.csv")
PNBN$Date <- as.Date(PNBN$Date, format="%Y-%m-%d")
PNBN$Close <- as.numeric(PNBN$Close)## Warning: NAs introduced by coercionPNBN$Volume <- as.integer(PNBN$Volume)## Warning: NAs introduced by coercionstr(PNBN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "760.000000" "750.000000" "750.000000" "770.000000" ...
## $ High : chr "780.000000" "770.000000" "780.000000" "770.000000" ...
## $ Low : chr "760.000000" "750.000000" "750.000000" "750.000000" ...
## $ Close : num 760 750 750 770 760 760 760 760 780 800 ...
## $ Adj.Close: chr "751.005920" "741.124268" "741.124268" "760.887573" ...
## $ Volume : int 1307500 391000 764500 2335000 1688500 1509000 12379500 3835500 4434500 2 ...colSums(is.na(PNBN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Paninvest Tbk (PNIN)
PNIN <- read.csv(file="Data Saham/PNIN.JK.csv")
PNIN$Date <- as.Date(PNIN$Date, format="%Y-%m-%d")
PNIN$Close <- as.numeric(PNIN$Close)## Warning: NAs introduced by coercionPNIN$Volume <- as.integer(PNIN$Volume)## Warning: NAs introduced by coercionstr(PNIN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "415.000000" "420.000000" "425.000000" "420.000000" ...
## $ High : chr "415.000000" "420.000000" "425.000000" "420.000000" ...
## $ Low : chr "410.000000" "415.000000" "415.000000" "415.000000" ...
## $ Close : num 415 420 425 420 420 420 420 425 440 455 ...
## $ Adj.Close: chr "409.581573" "414.516266" "419.450989" "414.516266" ...
## $ Volume : int 414500 347500 571500 198000 602000 1327500 965000 4067000 2091500 3 ...colSums(is.na(PNIN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Pembangunan Perumahan (Persero) Tbk (PTPP)
PTPP <- read.csv(file="Data Saham/PTPP.JK.csv")
PTPP$Date <- as.Date(PTPP$Date, format="%Y-%m-%d")
PTPP$Close <- as.numeric(PTPP$Close)## Warning: NAs introduced by coercionPTPP$Volume <- as.integer(PTPP$Volume)## Warning: NAs introduced by coercionstr(PTPP)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "451.475861" "456.228241" "460.980591" "451.475861" ...
## $ High : chr "460.980591" "460.980591" "465.732971" "460.980591" ...
## $ Low : chr "451.475861" "451.475861" "456.228241" "451.475861" ...
## $ Close : num 451 456 461 451 456 ...
## $ Adj.Close: chr "377.112213" "381.081818" "385.051422" "377.112213" ...
## $ Volume : int 930586 4178435 6999128 2500327 3313078 2039505 5790259 2448248 2238353 2 ...colSums(is.na(PTPP))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT SAT Nusapersada (PTSN)
PTSN <- read.csv(file="Data Saham/PTSN.JK.csv")
PTSN$Date <- as.Date(PTSN$Date, format="%Y-%m-%d")
PTSN$Close <- as.numeric(PTSN$Close)
PTSN$Volume <- as.integer(PTSN$Volume)
str(PTSN)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 26.7 26.7 26 26 26 ...
## $ High : num 26.7 26.7 26 26 26 ...
## $ Low : num 25.3 26.7 26 26 26 ...
## $ Close : num 26.7 26.7 26 26 26 ...
## $ Adj.Close: num 26.5 26.5 25.9 25.9 25.9 ...
## $ Volume : int 4500 4500 4500 4500 4500 4500 82500 214500 1500 3 ...colSums(is.na(PTSN))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Pakuwon Jati Tbk (PWON)
PWON <- read.csv(file="Data Saham/PWON.JK.csv")
PWON$Date <- as.Date(PWON$Date, format="%Y-%m-%d")
PWON$Close <- as.numeric(PWON$Close)## Warning: NAs introduced by coercionPWON$Volume <- as.integer(PWON$Volume)## Warning: NAs introduced by coercionstr(PWON)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "190.000000" "190.000000" "192.500000" "190.000000" ...
## $ High : chr "192.500000" "195.000000" "197.500000" "195.000000" ...
## $ Low : chr "185.000000" "187.500000" "187.500000" "187.500000" ...
## $ Close : num 190 190 192 190 192 ...
## $ Adj.Close: chr "174.523804" "174.523804" "176.820175" "174.523804" ...
## $ Volume : int 40454000 72062000 85502000 29246000 34004000 30696000 40916000 13540000 16756000 12 ...colSums(is.na(PWON))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Pyridam Farma Tbk (PYFA)
PYFA <- read.csv(file="Data Saham/PYFA.JK.csv")
PYFA$Date <- as.Date(PYFA$Date, format="%Y-%m-%d")
PYFA$Close <- as.numeric(PYFA$Close)## Warning: NAs introduced by coercionPYFA$Volume <- as.integer(PYFA$Volume)## Warning: NAs introduced by coercionstr(PYFA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "176.000000" "215.000000" "199.000000" "191.000000" ...
## $ High : chr "176.000000" "220.000000" "255.000000" "200.000000" ...
## $ Low : chr "175.000000" "177.000000" "198.000000" "190.000000" ...
## $ Close : num 176 215 199 191 191 193 195 194 193 194 ...
## $ Adj.Close: chr "169.972244" "207.636551" "192.184525" "184.458511" ...
## $ Volume : int 91000 14645000 144723500 4672000 2545500 1031500 1195500 6653500 606000 3 ...colSums(is.na(PYFA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Ricky Putra Globalindo (RICY)
RICY <- read.csv(file="Data Saham/RICY.JK.csv")
RICY$Date <- as.Date(RICY$Date, format="%Y-%m-%d")
RICY$Close <- as.numeric(RICY$Close)## Warning: NAs introduced by coercionRICY$Volume <- as.integer(RICY$Volume)## Warning: NAs introduced by coercionstr(RICY)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "183.000000" "183.000000" "183.000000" "184.000000" ...
## $ High : chr "183.000000" "187.000000" "190.000000" "189.000000" ...
## $ Low : chr "183.000000" "183.000000" "183.000000" "184.000000" ...
## $ Close : num 183 183 183 184 188 188 187 187 187 187 ...
## $ Adj.Close: chr "155.699005" "155.699005" "155.699005" "156.549820" ...
## $ Volume : int 53000 137000 469500 1707000 800000 1215500 226500 369500 247000 4 ...colSums(is.na(RICY))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Steady Safe Tbk (SAFE)
SAFE <- read.csv(file="Data Saham/SAFE.JK.csv")
SAFE$Date <- as.Date(SAFE$Date, format="%Y-%m-%d")
SAFE$Close <- as.numeric(SAFE$Close)## Warning: NAs introduced by coercionSAFE$Volume <- as.integer(SAFE$Volume)## Warning: NAs introduced by coercionstr(SAFE)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "87.000000" "87.000000" "87.000000" "87.000000" ...
## $ High : chr "87.000000" "87.000000" "87.000000" "87.000000" ...
## $ Low : chr "87.000000" "87.000000" "87.000000" "87.000000" ...
## $ Close : num 87 87 87 87 87 87 90 90 90 90 ...
## $ Adj.Close: chr "87.000000" "87.000000" "87.000000" "87.000000" ...
## $ Volume : int 500 500 500 500 500 500 6000 3000 3000 0 ...colSums(is.na(SAFE))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 8 0 8# PT Sidomulyo Selaras Tbk (SDMU)
SDMU <- read.csv(file="Data Saham/SDMU.JK.csv")
SDMU$Date <- as.Date(SDMU$Date, format="%Y-%m-%d")
SDMU$Close <- as.numeric(SDMU$Close)## Warning: NAs introduced by coercionSDMU$Volume <- as.integer(SDMU$Volume)## Warning: NAs introduced by coercionstr(SDMU)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "240.000000" "244.000000" "244.000000" "244.000000" ...
## $ High : chr "240.000000" "244.000000" "244.000000" "244.000000" ...
## $ Low : chr "240.000000" "236.000000" "228.000000" "240.000000" ...
## $ Close : num 240 244 244 244 240 240 240 240 240 240 ...
## $ Adj.Close: chr "233.941544" "237.840561" "237.840561" "237.840561" ...
## $ Volume : int 100625 401875 315625 153125 384375 330000 71250 329375 333750 0 ...colSums(is.na(SDMU))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Hotel Sahid Jaya International Tbk (SHID)
SHID <- read.csv(file="Data Saham/SHID.JK (1).csv")
SHID$Date <- as.Date(SHID$Date, format="%Y-%m-%d")
SHID$Close <- as.numeric(SHID$Close)
SHID$Volume <- as.integer(SHID$Volume)
str(SHID)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 415 415 390 395 395 395 380 385 370 395 ...
## $ High : num 415 415 405 395 395 395 380 395 380 400 ...
## $ Low : num 400 415 390 390 395 395 380 365 370 380 ...
## $ Close : num 415 415 390 395 395 395 380 385 370 395 ...
## $ Adj.Close: num 407 407 383 387 387 ...
## $ Volume : int 25000 25000 11500 3500 3500 3500 2000 92000 30500 1 ...colSums(is.na(SHID))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Suryamas Dutamakmur Tbk (SMDM)
SMDM <- read.csv(file="Data Saham/SMDM.JK.csv")
SMDM$Date <- as.Date(SMDM$Date, format="%Y-%m-%d")
SMDM$Close <- as.numeric(SMDM$Close)## Warning: NAs introduced by coercionSMDM$Volume <- as.integer(SMDM$Volume)## Warning: NAs introduced by coercionstr(SMDM)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "131.000000" "133.000000" "140.000000" "140.000000" ...
## $ High : chr "131.000000" "133.000000" "140.000000" "140.000000" ...
## $ Low : chr "131.000000" "132.000000" "134.000000" "140.000000" ...
## $ Close : num 131 133 140 140 136 146 150 160 147 150 ...
## $ Adj.Close: chr "131.000000" "133.000000" "140.000000" "140.000000" ...
## $ Volume : int 8500 3500 103000 103000 46000 452500 18000 6500 30500 3 ...colSums(is.na(SMDM))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Samudera Indonesia Tbk (SMDR)
SMDR <- read.csv(file="Data Saham/SMDR.JK.csv")
SMDR$Date <- as.Date(SMDR$Date, format="%Y-%m-%d")
SMDR$Close <- as.numeric(SMDR$Close)
SMDR$Volume <- as.integer(SMDR$Volume)
str(SMDR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 37 37.2 37.5 37.5 38 ...
## $ High : num 37.2 37.2 37.5 38.5 38.5 ...
## $ Low : num 37 35 37.2 35.8 37.5 ...
## $ Close : num 37 37.2 37.5 37.5 38 ...
## $ Adj.Close: num 19.9 20 20.1 20.1 20.4 ...
## $ Volume : int 300000 700000 100000 1100000 1400000 1400000 1400000 1400000 100000 0 ...colSums(is.na(SMDR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Semen Indonesia (Persero) Tbk (SMGR)
SMGR <- read.csv(file="Data Saham/SMGR.JK.csv")
SMGR$Date <- as.Date(SMGR$Date, format="%Y-%m-%d")
SMGR$Close <- as.numeric(SMGR$Close)## Warning: NAs introduced by coercionSMGR$Volume <- as.integer(SMGR$Volume)## Warning: NAs introduced by coercionstr(SMGR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "11200.000000" "11300.000000" "11100.000000" "11350.000000" ...
## $ High : chr "11450.000000" "11300.000000" "11400.000000" "11650.000000" ...
## $ Low : chr "11050.000000" "11000.000000" "11000.000000" "10900.000000" ...
## $ Close : num 11200 11300 11100 11350 10900 ...
## $ Adj.Close: chr "8689.724609" "8767.313477" "8612.137695" "8806.104492" ...
## $ Volume : int 580500 1902000 7390500 9634000 14246000 6370500 8444000 8439000 12719000 3 ...colSums(is.na(SMGR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Sejahreraraya Anugrahjaya Tbk (SRAJ)
SRAJ <- read.csv(file="Data Saham/SRAJ.JK.csv")
SRAJ$Date <- as.Date(SRAJ$Date, format="%Y-%m-%d")
SRAJ$Close <- as.numeric(SRAJ$Close)## Warning: NAs introduced by coercionSRAJ$Volume <- as.integer(SRAJ$Volume)## Warning: NAs introduced by coercionstr(SRAJ)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "319.667908" "319.667908" "351.240051" "319.667908" ...
## $ High : chr "319.667908" "319.667908" "359.133087" "323.614441" ...
## $ Low : chr "319.667908" "319.667908" "327.560944" "311.774872" ...
## $ Close : num 320 320 351 320 324 ...
## $ Adj.Close: chr "319.667908" "319.667908" "351.240051" "319.667908" ...
## $ Volume : int 8868 8868 6334 18370 1900 1266 1266 1266 1266 0 ...colSums(is.na(SRAJ))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Siantar Top Tbk (STTP)
STTP <- read.csv(file="Data Saham/STTP.JK.csv")
STTP$Date <- as.Date(STTP$Date, format="%Y-%m-%d")
STTP$Close <- as.numeric(STTP$Close)
STTP$Volume <- as.integer(STTP$Volume)
str(STTP)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 690 690 690 690 690 690 690 720 720 720 ...
## $ High : num 690 690 690 690 690 690 690 720 720 720 ...
## $ Low : num 690 690 690 690 690 690 690 700 720 720 ...
## $ Close : num 690 690 690 690 690 690 690 720 720 720 ...
## $ Adj.Close: num 684 684 684 684 684 ...
## $ Volume : int 25000 25000 25000 25000 25000 25000 25000 140000 140000 0 ...colSums(is.na(STTP))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Tunas Baru Lampung Tbk (TBLA)
TBLA <- read.csv(file="Data Saham/TBLA.JK.csv")
TBLA$Date <- as.Date(TBLA$Date, format="%Y-%m-%d")
TBLA$Close <- as.numeric(TBLA$Close)## Warning: NAs introduced by coercionTBLA$Volume <- as.integer(TBLA$Volume)## Warning: NAs introduced by coercionstr(TBLA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "600.000000" "610.000000" "600.000000" "590.000000" ...
## $ High : chr "600.000000" "610.000000" "630.000000" "620.000000" ...
## $ Low : chr "590.000000" "600.000000" "600.000000" "590.000000" ...
## $ Close : num 600 610 600 590 600 600 600 600 610 610 ...
## $ Adj.Close: chr "381.435059" "387.792297" "381.435059" "375.077850" ...
## $ Volume : int 228500 741000 3247500 3054000 1138000 1401500 3272500 4583500 8817500 4 ...colSums(is.na(TBLA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Telkom Indonesia (Persero) Tbk (TLKM)
TLKM <- read.csv(file="Data Saham/TLKM.JK (1).csv")
TLKM$Date <- as.Date(TLKM$Date, format="%Y-%m-%d")
TLKM$Close <- as.numeric(TLKM$Close)## Warning: NAs introduced by coercionTLKM$Volume <- as.integer(TLKM$Volume)## Warning: NAs introduced by coercionstr(TLKM)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "1410.000000" "1410.000000" "1420.000000" "1410.000000" ...
## $ High : chr "1420.000000" "1420.000000" "1430.000000" "1430.000000" ...
## $ Low : chr "1400.000000" "1400.000000" "1400.000000" "1400.000000" ...
## $ Close : num 1410 1410 1420 1410 1390 1410 1420 1400 1400 1390 ...
## $ Adj.Close: chr "1080.269043" "1080.269043" "1087.930542" "1080.269043" ...
## $ Volume : int 15267500 64482500 73910000 65095000 136315000 82305000 100222500 82470000 43985000 15 ...colSums(is.na(TLKM))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Temas Tbk (TMAS)
TMAS <- read.csv(file="Data Saham/TMAS.JK.csv")
TMAS$Date <- as.Date(TMAS$Date, format="%Y-%m-%d")
TMAS$Close <- as.numeric(TMAS$Close)
TMAS$Volume <- as.integer(TMAS$Volume)
str(TMAS)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 39.4 38.2 38.6 38.8 38.4 ...
## $ High : num 39.8 39.6 39.8 39 38.4 ...
## $ Low : num 39.4 38.2 38.6 38.4 38.4 ...
## $ Close : num 39.4 38.2 38.6 38.8 38.4 ...
## $ Adj.Close: num 28.5 27.7 28 28.1 27.8 ...
## $ Volume : int 100000 147500 215000 430000 50000 50000 500000 20000 30000 0 ...colSums(is.na(TMAS))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT Sarana Menara Nusantara Tbk (TOWR)
TOWR <- read.csv(file="Data Saham/TOWR.JK.csv")
TOWR$Date <- as.Date(TOWR$Date, format="%Y-%m-%d")
TOWR$Close <- as.numeric(TOWR$Close)## Warning: NAs introduced by coercionTOWR$Volume <- as.integer(TOWR$Volume)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion to integer rangestr(TOWR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "200.000000" "200.000000" "200.000000" "200.000000" ...
## $ High : chr "200.000000" "200.000000" "200.000000" "200.000000" ...
## $ Low : chr "200.000000" "200.000000" "200.000000" "200.000000" ...
## $ Close : num 200 200 200 200 200 200 200 200 200 206 ...
## $ Adj.Close: chr "167.095428" "167.095428" "167.095428" "167.095428" ...
## $ Volume : int 25000 25000 275000 3350000 5000000 125000 50000 400000 4500000 150 ...colSums(is.na(TOWR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 2# PT Tempo Scan Pacific (TSPC)
TSPC <- read.csv(file="Data Saham/TSPC.JK.csv")
TSPC$Date <- as.Date(TSPC$Date, format="%Y-%m-%d")
TSPC$Close <- as.numeric(TSPC$Close)
TSPC$Volume <- as.integer(TSPC$Volume)
str(TSPC)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 2550 2525 2550 2525 2525 ...
## $ High : num 2550 2525 2550 2525 2525 ...
## $ Low : num 2525 2500 2500 2525 2475 ...
## $ Close : num 2550 2525 2550 2525 2525 ...
## $ Adj.Close: num 1794 1777 1794 1777 1777 ...
## $ Volume : int 63000 32000 93500 500 60500 27000 33000 52500 45000 3 ...colSums(is.na(TSPC))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT United Tractors Tbk (UNTR)
UNTR <- read.csv(file="Data Saham/UNTR.JK.csv")
UNTR$Date <- as.Date(UNTR$Date, format="%Y-%m-%d")
UNTR$Close <- as.numeric(UNTR$Close)## Warning: NAs introduced by coercionUNTR$Volume <- as.integer(UNTR$Volume)## Warning: NAs introduced by coercionstr(UNTR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "26000.000000" "26400.000000" "27450.000000" "27300.000000" ...
## $ High : chr "26350.000000" "26400.000000" "27550.000000" "27400.000000" ...
## $ Low : chr "25950.000000" "26150.000000" "26700.000000" "27150.000000" ...
## $ Close : num 26000 26400 27450 27300 26800 ...
## $ Adj.Close: chr "16809.292969" "17067.902344" "17746.740234" "17649.761719" ...
## $ Volume : int 757000 3324000 4297500 2565500 2225000 4326000 9779000 4059500 2520500 3 ...colSums(is.na(UNTR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Unilever Indonesia Tbk (UNVR)
UNVR <- read.csv(file="Data Saham/UNVR.JK.csv")
UNVR$Date <- as.Date(UNVR$Date, format="%Y-%m-%d")
UNVR$Close <- as.numeric(UNVR$Close)
UNVR$Volume <- as.integer(UNVR$Volume)
str(UNVR)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 3710 3700 3860 4010 3840 3920 3960 3860 3940 3990 ...
## $ High : num 3770 3740 3880 4020 3960 3940 3990 4000 3950 4000 ...
## $ Low : num 3700 3640 3710 3840 3840 3860 3930 3860 3910 3920 ...
## $ Close : num 3710 3700 3860 4010 3840 3920 3960 3860 3940 3990 ...
## $ Adj.Close: num 2847 2839 2962 3077 2947 ...
## $ Volume : int 1415000 3997500 13172500 12265000 7035000 8315000 9477500 7850000 5007500 15 ...colSums(is.na(UNVR))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0# PT WEHA Transportasi Indonesia Tbk (WEHA)
WEHA <- read.csv(file="Data Saham/WEHA.JK (1).csv")
WEHA$Date <- as.Date(WEHA$Date, format="%Y-%m-%d")
WEHA$Close <- as.numeric(WEHA$Close)## Warning: NAs introduced by coercionWEHA$Volume <- as.integer(WEHA$Volume)## Warning: NAs introduced by coercionstr(WEHA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "159.374954" "160.312454" "160.312454" "160.312454" ...
## $ High : chr "159.374954" "160.312454" "160.312454" "160.312454" ...
## $ Low : chr "159.374954" "159.374954" "160.312454" "160.312454" ...
## $ Close : num 159 160 160 160 160 ...
## $ Adj.Close: chr "157.351105" "158.276688" "158.276688" "158.276688" ...
## $ Volume : int 697066 800000 693333 693333 698666 697600 732266 700800 694400 0 ...colSums(is.na(WEHA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Wintermar Offshore Marine Tbk (WINS)
WINS <- read.csv(file="Data Saham/WINS.JK.csv")
WINS$Date <- as.Date(WINS$Date, format="%Y-%m-%d")
WINS$Close <- as.numeric(WINS$Close)## Warning: NAs introduced by coercionWINS$Volume <- as.integer(WINS$Volume)## Warning: NAs introduced by coercionstr(WINS)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "312.874695" "322.652039" "322.652039" "317.763367" ...
## $ High : chr "322.652039" "327.540680" "322.652039" "322.652039" ...
## $ Low : chr "307.986023" "312.874695" "317.763367" "312.874695" ...
## $ Close : num 313 323 323 318 313 ...
## $ Adj.Close: chr "306.019897" "315.583008" "315.583008" "310.801453" ...
## $ Volume : int 4158086 16016635 3966827 15140630 3698349 4010806 9987384 34072682 10619459 3 ...colSums(is.na(WINS))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Yanaprima Hastapersada Tbk (YPAS)
YPAS <- read.csv(file="Data Saham/YPAS.JK.csv")
YPAS$Date <- as.Date(YPAS$Date, format="%Y-%m-%d")
YPAS$Close <- as.numeric(YPAS$Close)## Warning: NAs introduced by coercionYPAS$Volume <- as.integer(YPAS$Volume)## Warning: NAs introduced by coercionstr(YPAS)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : chr "670.000000" "680.000000" "690.000000" "680.000000" ...
## $ High : chr "690.000000" "680.000000" "690.000000" "700.000000" ...
## $ Low : chr "670.000000" "660.000000" "670.000000" "680.000000" ...
## $ Close : num 670 680 690 680 670 660 660 670 660 670 ...
## $ Adj.Close: chr "670.000000" "680.000000" "690.000000" "680.000000" ...
## $ Volume : int 63000 62000 63000 59000 64000 66500 70000 69000 64000 3 ...colSums(is.na(YPAS))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 1 0 1# PT Dosni Roha Indonesia Tbk (ZBRA)
ZBRA <- read.csv(file="Data Saham/ZBRA.JK.csv")
ZBRA$Date <- as.Date(ZBRA$Date, format="%Y-%m-%d")
ZBRA$Close <- as.numeric(ZBRA$Close)
ZBRA$Volume <- as.integer(ZBRA$Volume)
str(ZBRA)## 'data.frame': 2733 obs. of 7 variables:
## $ Date : Date, format: "2012-01-02" "2012-01-03" ...
## $ Open : num 50 50 50 50 50 50 50 50 50 50 ...
## $ High : num 51 50 50 50 50 50 50 50 50 50 ...
## $ Low : num 50 50 50 50 50 50 50 50 50 50 ...
## $ Close : num 50 50 50 50 50 50 50 50 50 50 ...
## $ Adj.Close: num 50 50 50 50 50 50 50 50 50 50 ...
## $ Volume : int 17000 17000 3000 3000 3000 250000 250000 250000 5000 0 ...colSums(is.na(ZBRA))## Date Open High Low Close Adj.Close Volume
## 0 0 0 0 0 0 0Pada tahap ini, data frame untuk setiap saham diatur ulang untuk membantu penulis membuat 3 kolom yang diperlukan untuk melakukan korelasi antara Daily Return saham dengan Liquidity saham. Untuk melakukannya, penulis pertama mengurut ulang data harga saham menjadi dari yang paling terbaru hingga paling awal. Setelah urutan data telah dibalik, dibuatlah 3 kolom yang diperlukan: -Daily Return (dret) menunjukkan persentase besarnya perbedaan harga saham dari hari (n) dengan hari (n-1) -Dollar Value (DolValue) menunjukkan hasil perkalian harga penutupan saham (close) dengan jumlah transaksi saham pada hari yang sama (Volume) -Liquidity (Liquid) menunjukkan ratio antara Daily Return dan Dollar Value sebuah saham.
Selain itu, kolom-kolom yang memiliki informasi yang tidak dibutuhkan pada artikel ini diassign sebagai NULL untuk menghilangkan kolom-kolom tersebut dari data frame saham. Kolom-kolom yang tidak dibutuhkan adalah Harga Pembukaan (Open), Harga Pembukaan (Open), Harga Tertinggi (High), Harga Terendah (Low), dan Harga Penutupan yang Disesuaikan (Adj. Close).
Bisa dilihat juga bahwa pada beberapa chunk terdapat fungsi do.call. Ini digunakan untuk keperluan Descriptive Statistics nanti agar tidak menunjukkan hasil “NaN”, “NA”, atau “Inf (infinite)” yang bisa muncul akibat adanya pembagian sebuah angka dengan angka 0. Hasil perkalian dengan angka 0 akan menunjukkan nilai “Inf”. Dengan fungsi do.call tersebut, hasil perhitungan yang menunjukkan nilai “Inf” akan diganti menjadi NA.
AALI <- AALI[order(AALI$Date,decreasing =T),]
AALI$Open <- NULL
AALI$High <- NULL
AALI$Low <- NULL
AALI$Adj.Close <- NULL
AALI$Daily_Return <- NULL
AALI$dret <- c(-diff(AALI$Close)/AALI$Close[-1]*100,NA)
AALI$DolValue <- AALI$Close * AALI$Volume
AALI$Liquid <- AALI$dret/AALI$DolValue
tail(AALI)ABBA <- ABBA[order(ABBA$Date,decreasing=T),]
ABBA$Open <- NULL
ABBA$High <- NULL
ABBA$Low <- NULL
ABBA$Adj.Close <- NULL
ABBA$Daily_Return <- NULL
ABBA$dret <- c(-diff(ABBA$Close)/ABBA$Close[-1]*100,NA)
ABBA$DolValue <- ABBA$Close * ABBA$Volume
ABBA$Liquid <- ABBA$dret/ABBA$DolValue
ABBA<- do.call(data.frame,
lapply(ABBA,function(x) replace(x, is.infinite(x), NA)))
tail(ABBA)ABMM <- ABMM[order(ABMM$Date,decreasing=T),]
ABMM$Open <- NULL
ABMM$High <- NULL
ABMM$Low <- NULL
ABMM$Adj.Close <- NULL
ABMM$Daily_Return <- NULL
ABMM$dret <- c(-diff(ABMM$Close)/ABMM$Close[-1]*100,NA)
ABMM$DolValue <- ABMM$Close * ABMM$Volume
ABMM$Liquid <- ABMM$dret/ABMM$DolValue
ABMM<- do.call(data.frame,
lapply(ABMM,function(x) replace(x, is.infinite(x), NA)))
tail(ABMM)ACES <- ACES[order(ACES$Date,decreasing=T),]
ACES$Open <- NULL
ACES$High <- NULL
ACES$Low <- NULL
ACES$Adj.Close <- NULL
ACES$dret <- c(-diff(ACES$Close)/ACES$Close[-1]*100,NA)
ACES$DolValue <- ACES$Close * ACES$Volume
ACES$Liquid <- ACES$dret/ACES$DolValue
tail(ACES)ADES <- ADES[order(ADES$Date,decreasing=T),]
ADES$Open <- NULL
ADES$High <- NULL
ADES$Low <- NULL
ADES$Adj.Close <- NULL
ADES$dret <- c(-diff(ADES$Close)/ADES$Close[-1]*100,NA)
ADES$DolValue <- ADES$Close * ADES$Volume
ADES$Liquid <- ADES$dret/ADES$DolValue
tail(ADES)ADHI <- ADHI[order(ADHI$Date,decreasing=T),]
ADHI$Open <- NULL
ADHI$High <- NULL
ADHI$Low <- NULL
ADHI$Adj.Close <- NULL
ADHI$dret <- c(-diff(ADHI$Close)/ADHI$Close[-1]*100,NA)
ADHI$DolValue <- ADHI$Close * ADHI$Volume
ADHI$Liquid <- ADHI$dret/ADHI$DolValue
tail(ADHI)ADMG <- ADMG[order(ADMG$Date,decreasing=T),]
ADMG$Open <- NULL
ADMG$High <- NULL
ADMG$Low <- NULL
ADMG$Adj.Close <- NULL
ADMG$dret <- c(-diff(ADMG$Close)/ADMG$Close[-1]*100,NA)
ADMG$DolValue <- ADMG$Close * ADMG$Volume
ADMG$Liquid <- ADMG$dret/ADMG$DolValue
tail(ADMG)ADRO <- ADRO[order(ADRO$Date,decreasing=T),]
ADRO$Open <- NULL
ADRO$High <- NULL
ADRO$Low <- NULL
ADRO$Adj.Close <- NULL
ADRO$dret <- c(-diff(ADRO$Close)/ADRO$Close[-1]*100,NA)
ADRO$DolValue <- ADRO$Close * ADRO$Volume
ADRO$Liquid <- ADRO$dret/ADRO$DolValue
tail(ADRO)AGRO <- AGRO[order(AGRO$Date,decreasing=T),]
AGRO$Open <- NULL
AGRO$High <- NULL
AGRO$Low <- NULL
AGRO$Adj.Close <- NULL
AGRO$dret <- c(-diff(AGRO$Close)/AGRO$Close[-1]*100,NA)
AGRO$DolValue <- AGRO$Close * AGRO$Volume
AGRO$Liquid <- AGRO$dret/AGRO$DolValue
tail(AGRO)AISA <- AISA[order(AISA$Date,decreasing=T),]
AISA$Open <- NULL
AISA$High <- NULL
AISA$Low <- NULL
AISA$Adj.Close <- NULL
AISA$dret <- c(-diff(AISA$Close)/AISA$Close[-1]*100,NA)
AISA$DolValue <- AISA$Close * AISA$Volume
AISA$Liquid <- AISA$dret/AISA$DolValue
tail(AISA)AKPI <- AKPI[order(AKPI$Date,decreasing=T),]
AKPI$Open <- NULL
AKPI$High <- NULL
AKPI$Low <- NULL
AKPI$Adj.Close <- NULL
AKPI$dret <- c(-diff(AKPI$Close)/AKPI$Close[-1]*100,NA)
AKPI$DolValue <- AKPI$Close * AKPI$Volume
AKPI$Liquid <- AKPI$dret/AKPI$DolValue
AKPI<- do.call(data.frame,
lapply(AKPI,function(x) replace(x, is.infinite(x), NA)))
tail(AKPI)AKRA <- AKRA[order(AKRA$Date,decreasing=T),]
AKRA$Open <- NULL
AKRA$High <- NULL
AKRA$Low <- NULL
AKRA$Adj.Close <- NULL
AKRA$dret <- c(-diff(AKRA$Close)/AKRA$Close[-1]*100,NA)
AKRA$DolValue <- AKRA$Close * AKRA$Volume
AKRA$Liquid <- AKRA$dret/AKRA$DolValue
tail(AKRA)AKSI <- AKSI[order(AKSI$Date,decreasing=T),]
AKSI$Open <- NULL
AKSI$High <- NULL
AKSI$Low <- NULL
AKSI$Adj.Close <- NULL
AKSI$dret <- c(-diff(AKSI$Close)/AKSI$Close[-1]*100,NA)
AKSI$DolValue <- AKSI$Close * AKSI$Volume
AKSI$Liquid <- AKSI$dret/AKSI$DolValue
tail(AKSI)ALDO <- ALDO[order(ALDO$Date,decreasing=T),]
ALDO$Open <- NULL
ALDO$High <- NULL
ALDO$Low <- NULL
ALDO$Adj.Close <- NULL
ALDO$dret <- c(-diff(ALDO$Close)/ALDO$Close[-1]*100,NA)
ALDO$DolValue <- ALDO$Close * ALDO$Volume
ALDO$Liquid <- ALDO$dret/ALDO$DolValue
tail(ALDO)ALMI <- ALMI[order(ALMI$Date,decreasing=T),]
ALMI$Open <- NULL
ALMI$High <- NULL
ALMI$Low <- NULL
ALMI$Adj.Close <- NULL
ALMI$dret <- c(-diff(ALMI$Close)/ALMI$Close[-1]*100,NA)
ALMI$DolValue <- ALMI$Close * ALMI$Volume
ALMI$Liquid <- ALMI$dret/ALMI$DolValue
ALMI<- do.call(data.frame,
lapply(ALMI,function(x) replace(x, is.infinite(x), NA)))
tail(ALMI)AMFG <- AMFG[order(AMFG$Date,decreasing=T),]
AMFG$Open <- NULL
AMFG$High <- NULL
AMFG$Low <- NULL
AMFG$Adj.Close <- NULL
AMFG$dret <- c(-diff(AMFG$Close)/AMFG$Close[-1]*100,NA)
AMFG$DolValue <- AMFG$Close * AMFG$Volume
AMFG$Liquid <- AMFG$dret/AMFG$DolValue
AMFG<- do.call(data.frame,
lapply(AMFG,function(x) replace(x, is.infinite(x), NA)))
tail(AMFG)AMRT <- AMRT[order(AMRT$Date,decreasing=T),]
AMRT$Open <- NULL
AMRT$High <- NULL
AMRT$Low <- NULL
AMRT$Adj.Close <- NULL
AMRT$dret <- c(-diff(AMRT$Close)/AMRT$Close[-1]*100,NA)
AMRT$DolValue <- AMRT$Close * AMRT$Volume
AMRT$Liquid <- AMRT$dret/AMRT$DolValue
tail(AMRT)ANTM <- ANTM[order(ANTM$Date,decreasing=T),]
ANTM$Open <- NULL
ANTM$High <- NULL
ANTM$Low <- NULL
ANTM$Adj.Close <- NULL
ANTM$dret <- c(-diff(ANTM$Close)/ANTM$Close[-1]*100,NA)
ANTM$DolValue <- ANTM$Close * ANTM$Volume
ANTM$Liquid <- ANTM$dret/ANTM$DolValue
tail(ANTM)APLN <- APLN[order(APLN$Date,decreasing=T),]
APLN$Open <- NULL
APLN$High <- NULL
APLN$Low <- NULL
APLN$Adj.Close <- NULL
APLN$dret <- c(-diff(APLN$Close)/APLN$Close[-1]*100,NA)
APLN$DolValue <- APLN$Close * APLN$Volume
APLN$Liquid <- APLN$dret/APLN$DolValue
tail(APLN)ASII <- ASII[order(ASII$Date,decreasing=T),]
ASII$Open <- NULL
ASII$High <- NULL
ASII$Low <- NULL
ASII$Adj.Close <- NULL
ASII$dret <- c(-diff(ASII$Close)/ASII$Close[-1]*100,NA)
ASII$DolValue <- ASII$Close * ASII$Volume
ASII$Liquid <- ASII$dret/ASII$DolValue
tail(ASII)ASRI <- ASRI[order(ASRI$Date,decreasing=T),]
ASRI$Open <- NULL
ASRI$High <- NULL
ASRI$Low <- NULL
ASRI$Adj.Close <- NULL
ASRI$dret <- c(-diff(ASRI$Close)/ASRI$Close[-1]*100,NA)
ASRI$DolValue <- ASRI$Close * ASRI$Volume
ASRI$Liquid <- ASRI$dret/ASRI$DolValue
tail(ASRI)AUTO <- AUTO[order(AUTO$Date,decreasing=T),]
AUTO$Open <- NULL
AUTO$High <- NULL
AUTO$Low <- NULL
AUTO$Adj.Close <- NULL
AUTO$dret <- c(-diff(AUTO$Close)/AUTO$Close[-1]*100,NA)
AUTO$DolValue <- AUTO$Close * AUTO$Volume
AUTO$Liquid <- AUTO$dret/AUTO$DolValue
tail(AUTO)BABP <- BABP[order(BABP$Date,decreasing=T),]
BABP$Open <- NULL
BABP$High <- NULL
BABP$Low <- NULL
BABP$Adj.Close <- NULL
BABP$dret <- c(-diff(BABP$Close)/BABP$Close[-1]*100,NA)
BABP$DolValue <- BABP$Close * BABP$Volume
BABP$Liquid <- BABP$dret/BABP$DolValue
tail(BABP)BAPA <- BAPA[order(BAPA$Date,decreasing=T),]
BAPA$Open <- NULL
BAPA$High <- NULL
BAPA$Low <- NULL
BAPA$Adj.Close <- NULL
BAPA$dret <- c(-diff(BAPA$Close)/BAPA$Close[-1]*100,NA)
BAPA$DolValue <- BAPA$Close * BAPA$Volume
BAPA$Liquid <- BAPA$dret/BAPA$DolValue
tail(BAPA)BAYU <- BAYU[order(BAYU$Date,decreasing=T),]
BAYU$Open <- NULL
BAYU$High <- NULL
BAYU$Low <- NULL
BAYU$Adj.Close <- NULL
BAYU$dret <- c(-diff(BAYU$Close)/BAYU$Close[-1]*100,NA)
BAYU$DolValue <- BAYU$Close * BAYU$Volume
BAYU$Liquid <- BAYU$dret/BAYU$DolValue
BAYU<- do.call(data.frame,
lapply(BAYU,function(x) replace(x, is.infinite(x), NA)))
tail(BAYU)BBCA <- BBCA[order(BBCA$Date,decreasing=T),]
BBCA$Open <- NULL
BBCA$High <- NULL
BBCA$Low <- NULL
BBCA$Adj.Close <- NULL
BBCA$dret <- c(-diff(BBCA$Close)/BBCA$Close[-1]*100,NA)
BBCA$DolValue <- BBCA$Close * BBCA$Volume
BBCA$Liquid <- BBCA$dret/BBCA$DolValue
tail(BBCA)BBRI <- BBRI[order(BBRI$Date,decreasing=T),]
BBRI$Open <- NULL
BBRI$High <- NULL
BBRI$Low <- NULL
BBRI$Adj.Close <- NULL
BBRI$dret <- c(-diff(BBRI$Close)/BBRI$Close[-1]*100,NA)
BBRI$DolValue <- BBRI$Close * BBRI$Volume
BBRI$Liquid <- BBRI$dret/BBRI$DolValue
tail(BBRI)BBTN <- BBTN[order(BBTN$Date,decreasing=T),]
BBTN$Open <- NULL
BBTN$High <- NULL
BBTN$Low <- NULL
BBTN$Adj.Close <- NULL
BBTN$dret <- c(-diff(BBTN$Close)/BBTN$Close[-1]*100,NA)
BBTN$DolValue <- BBTN$Close * BBTN$Volume
BBTN$Liquid <- BBTN$dret/BBTN$DolValue
tail(BBTN)BDMN <- BDMN[order(BDMN$Date,decreasing=T),]
BDMN$Open <- NULL
BDMN$High <- NULL
BDMN$Low <- NULL
BDMN$Adj.Close <- NULL
BDMN$dret <- c(-diff(BDMN$Close)/BDMN$Close[-1]*100,NA)
BDMN$DolValue <- BDMN$Close * BDMN$Volume
BDMN$Liquid <- BDMN$dret/BDMN$DolValue
tail(BDMN)BFIN <- BFIN[order(BFIN$Date,decreasing=T),]
BFIN$Open <- NULL
BFIN$High <- NULL
BFIN$Low <- NULL
BFIN$Adj.Close <- NULL
BFIN$dret <- c(-diff(BFIN$Close)/BFIN$Close[-1]*100,NA)
BFIN$DolValue <- BFIN$Close * BFIN$Volume
BFIN$Liquid <- BFIN$dret/BFIN$DolValue
tail(BFIN)BHIT <- BHIT[order(BHIT$Date,decreasing=T),]
BHIT$Open <- NULL
BHIT$High <- NULL
BHIT$Low <- NULL
BHIT$Adj.Close <- NULL
BHIT$dret <- c(-diff(BHIT$Close)/BHIT$Close[-1]*100,NA)
BHIT$DolValue <- BHIT$Close * BHIT$Volume
BHIT$Liquid <- BHIT$dret/BHIT$DolValue
tail(BHIT)BIPP <- BIPP[order(BIPP$Date,decreasing=T),]
BIPP$Open <- NULL
BIPP$High <- NULL
BIPP$Low <- NULL
BIPP$Adj.Close <- NULL
BIPP$dret <- c(-diff(BIPP$Close)/BIPP$Close[-1]*100,NA)
BIPP$DolValue <- BIPP$Close * BIPP$Volume
BIPP$Liquid <- BIPP$dret/BIPP$DolValue
tail(BIPP)BISI <- BISI[order(BISI$Date,decreasing=T),]
BISI$Open <- NULL
BISI$High <- NULL
BISI$Low <- NULL
BISI$Adj.Close <- NULL
BISI$dret <- c(-diff(BISI$Close)/BISI$Close[-1]*100,NA)
BISI$DolValue <- BISI$Close * BISI$Volume
BISI$Liquid <- BISI$dret/BISI$DolValue
tail(BISI)BKDP <- BKDP[order(BKDP$Date,decreasing=T),]
BKDP$Open <- NULL
BKDP$High <- NULL
BKDP$Low <- NULL
BKDP$Adj.Close <- NULL
BKDP$dret <- c(-diff(BKDP$Close)/BKDP$Close[-1]*100,NA)
BKDP$DolValue <- BKDP$Close * BKDP$Volume
BKDP$Liquid <- BKDP$dret/BKDP$DolValue
tail(BKDP)BKSL <- BKSL[order(BKSL$Date,decreasing=T),]
BKSL$Open <- NULL
BKSL$High <- NULL
BKSL$Low <- NULL
BKSL$Adj.Close <- NULL
BKSL$dret <- c(-diff(BKSL$Close)/BKSL$Close[-1]*100,NA)
BKSL$DolValue <- BKSL$Close * BKSL$Volume
BKSL$Liquid <- BKSL$dret/BKSL$DolValue
tail(BKSL)BMRI <- BMRI[order(BMRI$Date,decreasing=T),]
BMRI$Open <- NULL
BMRI$High <- NULL
BMRI$Low <- NULL
BMRI$Adj.Close <- NULL
BMRI$dret <- c(-diff(BMRI$Close)/BMRI$Close[-1]*100,NA)
BMRI$DolValue <- BMRI$Close * BMRI$Volume
BMRI$Liquid <- BMRI$dret/BMRI$DolValue
tail(BMRI)BMTR <- BMTR[order(BMTR$Date,decreasing=T),]
BMTR$Open <- NULL
BMTR$High <- NULL
BMTR$Low <- NULL
BMTR$Adj.Close <- NULL
BMTR$dret <- c(-diff(BMTR$Close)/BMTR$Close[-1]*100,NA)
BMTR$DolValue <- BMTR$Close * BMTR$Volume
BMTR$Liquid <- BMTR$dret/BMTR$DolValue
tail(BMTR)BNBA <- BNBA[order(BNBA$Date,decreasing=T),]
BNBA$Open <- NULL
BNBA$High <- NULL
BNBA$Low <- NULL
BNBA$Adj.Close <- NULL
BNBA$dret <- c(-diff(BNBA$Close)/BNBA$Close[-1]*100,NA)
BNBA$DolValue <- BNBA$Close * BNBA$Volume
BNBA$Liquid <- BNBA$dret/BNBA$DolValue
tail(BNBA)BNGA <- BNGA[order(BNGA$Date,decreasing=T),]
BNGA$Open <- NULL
BNGA$High <- NULL
BNGA$Low <- NULL
BNGA$Adj.Close <- NULL
BNGA$dret <- c(-diff(BNGA$Close)/BNGA$Close[-1]*100,NA)
BNGA$DolValue <- BNGA$Close * BNGA$Volume
BNGA$Liquid <- BNGA$dret/BNGA$DolValue
tail(BNGA)BSDE <- BSDE[order(BSDE$Date,decreasing=T),]
BSDE$Open <- NULL
BSDE$High <- NULL
BSDE$Low <- NULL
BSDE$Adj.Close <- NULL
BSDE$dret <- c(-diff(BSDE$Close)/BSDE$Close[-1]*100,NA)
BSDE$DolValue <- BSDE$Close * BSDE$Volume
BSDE$Liquid <- BSDE$dret/BSDE$DolValue
tail(BSDE)BTON <- BTON[order(BTON$Date,decreasing=T),]
BTON$Open <- NULL
BTON$High <- NULL
BTON$Low <- NULL
BTON$Adj.Close <- NULL
BTON$dret <- c(-diff(BTON$Close)/BTON$Close[-1]*100,NA)
BTON$DolValue <- BTON$Close * BTON$Volume
BTON$Liquid <- BTON$dret/BTON$DolValue
tail(BTON)BULL <- BULL[order(BULL$Date,decreasing=T),]
BULL$Open <- NULL
BULL$High <- NULL
BULL$Low <- NULL
BULL$Adj.Close <- NULL
BULL$dret <- c(-diff(BULL$Close)/BULL$Close[-1]*100,NA)
BULL$DolValue <- BULL$Close * BULL$Volume
BULL$Liquid <- BULL$dret/BULL$DolValue
BULL<- do.call(data.frame,
lapply(BULL,function(x) replace(x, is.infinite(x), NA)))
tail(BULL)BUMI <- BUMI[order(BUMI$Date,decreasing=T),]
BUMI$Open <- NULL
BUMI$High <- NULL
BUMI$Low <- NULL
BUMI$Adj.Close <- NULL
BUMI$dret <- c(-diff(BUMI$Close)/BUMI$Close[-1]*100,NA)
BUMI$DolValue <- BUMI$Close * BUMI$Volume
BUMI$Liquid <- BUMI$dret/BUMI$DolValue
tail(BUMI)CASS <- CASS[order(CASS$Date,decreasing=T),]
CASS$Open <- NULL
CASS$High <- NULL
CASS$Low <- NULL
CASS$Adj.Close <- NULL
CASS$dret <- c(-diff(CASS$Close)/CASS$Close[-1]*100,NA)
CASS$DolValue <- CASS$Close * CASS$Volume
CASS$Liquid <- CASS$dret/CASS$DolValue
CASS<- do.call(data.frame,
lapply(CASS,function(x) replace(x, is.infinite(x), NA)))
tail(CASS)CEKA <- CEKA[order(CEKA$Date,decreasing=T),]
CEKA$Open <- NULL
CEKA$High <- NULL
CEKA$Low <- NULL
CEKA$Adj.Close <- NULL
CEKA$dret <- c(-diff(CEKA$Close)/CEKA$Close[-1]*100,NA)
CEKA$DolValue <- CEKA$Close * CEKA$Volume
CEKA$Liquid <- CEKA$dret/CEKA$DolValue
CEKA<- do.call(data.frame,
lapply(CEKA,function(x) replace(x, is.infinite(x), NA)))
tail(CEKA)CENT <- CENT[order(CENT$Date,decreasing=T),]
CENT$Open <- NULL
CENT$High <- NULL
CENT$Low <- NULL
CENT$Adj.Close <- NULL
CENT$dret <- c(-diff(CENT$Close)/CENT$Close[-1]*100,NA)
CENT$DolValue <- CENT$Close * CENT$Volume
CENT$Liquid <- CENT$dret/CENT$DolValue
tail(CENT)CMNP <- CMNP[order(CMNP$Date,decreasing=T),]
CMNP$Open <- NULL
CMNP$High <- NULL
CMNP$Low <- NULL
CMNP$Adj.Close <- NULL
CMNP$dret <- c(-diff(CMNP$Close)/CMNP$Close[-1]*100,NA)
CMNP$DolValue <- CMNP$Close * CMNP$Volume
CMNP$Liquid <- CMNP$dret/CMNP$DolValue
tail(CMNP)CPIN <- CPIN[order(CPIN$Date,decreasing=T),]
CPIN$Open <- NULL
CPIN$High <- NULL
CPIN$Low <- NULL
CPIN$Adj.Close <- NULL
CPIN$dret <- c(-diff(CPIN$Close)/CPIN$Close[-1]*100,NA)
CPIN$DolValue <- CPIN$Close * CPIN$Volume
CPIN$Liquid <- CPIN$dret/CPIN$DolValue
tail(CPIN)CTRA <- CTRA[order(CTRA$Date,decreasing=T),]
CTRA$Open <- NULL
CTRA$High <- NULL
CTRA$Low <- NULL
CTRA$Adj.Close <- NULL
CTRA$dret <- c(-diff(CTRA$Close)/CTRA$Close[-1]*100,NA)
CTRA$DolValue <- CTRA$Close * CTRA$Volume
CTRA$Liquid <- CTRA$dret/CTRA$DolValue
tail(CTRA)DART <- DART[order(DART$Date,decreasing=T),]
DART$Open <- NULL
DART$High <- NULL
DART$Low <- NULL
DART$Adj.Close <- NULL
DART$dret <- c(-diff(DART$Close)/DART$Close[-1]*100,NA)
DART$DolValue <- DART$Close * DART$Volume
DART$Liquid <- DART$dret/DART$DolValue
DART<- do.call(data.frame,
lapply(DART,function(x) replace(x, is.infinite(x), NA)))
tail(DART)DGIK <- DGIK[order(DGIK$Date,decreasing=T),]
DGIK$Open <- NULL
DGIK$High <- NULL
DGIK$Low <- NULL
DGIK$Adj.Close <- NULL
DGIK$dret <- c(-diff(DGIK$Close)/DGIK$Close[-1]*100,NA)
DGIK$DolValue <- DGIK$Close * DGIK$Volume
DGIK$Liquid <- DGIK$dret/DGIK$DolValue
tail(DGIK)DOID <- DOID[order(DOID$Date,decreasing=T),]
DOID$Open <- NULL
DOID$High <- NULL
DOID$Low <- NULL
DOID$Adj.Close <- NULL
DOID$dret <- c(-diff(DOID$Close)/DOID$Close[-1]*100,NA)
DOID$DolValue <- DOID$Close * DOID$Volume
DOID$Liquid <- DOID$dret/DOID$DolValue
tail(DOID)DVLA <- DVLA[order(DVLA$Date,decreasing=T),]
DVLA$Open <- NULL
DVLA$High <- NULL
DVLA$Low <- NULL
DVLA$Adj.Close <- NULL
DVLA$dret <- c(-diff(DVLA$Close)/DVLA$Close[-1]*100,NA)
DVLA$DolValue <- DVLA$Close * DVLA$Volume
DVLA$Liquid <- DVLA$dret/DVLA$DolValue
tail(DVLA)ELSA <- ELSA[order(ELSA$Date,decreasing=T),]
ELSA$Open <- NULL
ELSA$High <- NULL
ELSA$Low <- NULL
ELSA$Adj.Close <- NULL
ELSA$dret <- c(-diff(ELSA$Close)/ELSA$Close[-1]*100,NA)
ELSA$DolValue <- ELSA$Close * ELSA$Volume
ELSA$Liquid <- ELSA$dret/ELSA$DolValue
tail(ELSA)EMTK <- EMTK[order(EMTK$Date,decreasing=T),]
EMTK$Open <- NULL
EMTK$High <- NULL
EMTK$Low <- NULL
EMTK$Adj.Close <- NULL
EMTK$dret <- c(-diff(EMTK$Close)/EMTK$Close[-1]*100,NA)
EMTK$DolValue <- EMTK$Close * EMTK$Volume
EMTK$Liquid <- EMTK$dret/EMTK$DolValue
EMTK<- do.call(data.frame,
lapply(EMTK,function(x) replace(x, is.infinite(x), NA)))
tail(EMTK)ERAA <- ERAA[order(ERAA$Date,decreasing=T),]
ERAA$Open <- NULL
ERAA$High <- NULL
ERAA$Low <- NULL
ERAA$Adj.Close <- NULL
ERAA$dret <- c(-diff(ERAA$Close)/ERAA$Close[-1]*100,NA)
ERAA$DolValue <- ERAA$Close * ERAA$Volume
ERAA$Liquid <- ERAA$dret/ERAA$DolValue
tail(ERAA)EXCL <- EXCL[order(EXCL$Date,decreasing=T),]
EXCL$Open <- NULL
EXCL$High <- NULL
EXCL$Low <- NULL
EXCL$Adj.Close <- NULL
EXCL$dret <- c(-diff(EXCL$Close)/EXCL$Close[-1]*100,NA)
EXCL$DolValue <- EXCL$Close * EXCL$Volume
EXCL$Liquid <- EXCL$dret/EXCL$DolValue
tail(EXCL)FPNI <- FPNI[order(FPNI$Date,decreasing=T),]
FPNI$Open <- NULL
FPNI$High <- NULL
FPNI$Low <- NULL
FPNI$Adj.Close <- NULL
FPNI$dret <- c(-diff(FPNI$Close)/FPNI$Close[-1]*100,NA)
FPNI$DolValue <- FPNI$Close * FPNI$Volume
FPNI$Liquid <- FPNI$dret/FPNI$DolValue
FPNI<- do.call(data.frame,
lapply(FPNI,function(x) replace(x, is.infinite(x), NA)))
tail(FPNI)FREN <- FREN[order(FREN$Date,decreasing=T),]
FREN$Open <- NULL
FREN$High <- NULL
FREN$Low <- NULL
FREN$Adj.Close <- NULL
FREN$dret <- c(-diff(FREN$Close)/FREN$Close[-1]*100,NA)
FREN$DolValue <- FREN$Close * FREN$Volume
FREN$Liquid <- FREN$dret/FREN$DolValue
tail(FREN)HRUM <- HRUM[order(HRUM$Date,decreasing=T),]
HRUM$Open <- NULL
HRUM$High <- NULL
HRUM$Low <- NULL
HRUM$Adj.Close <- NULL
HRUM$dret <- c(-diff(HRUM$Close)/HRUM$Close[-1]*100,NA)
HRUM$DolValue <- HRUM$Close * HRUM$Volume
HRUM$Liquid <- HRUM$dret/HRUM$DolValue
tail(HRUM)ICBP <- ICBP[order(ICBP$Date,decreasing=T),]
ICBP$Open <- NULL
ICBP$High <- NULL
ICBP$Low <- NULL
ICBP$Adj.Close <- NULL
ICBP$dret <- c(-diff(ICBP$Close)/ICBP$Close[-1]*100,NA)
ICBP$DolValue <- ICBP$Close * ICBP$Volume
ICBP$Liquid <- ICBP$dret/ICBP$DolValue
tail(ICBP)INAF <- INAF[order(INAF$Date,decreasing=T),]
INAF$Open <- NULL
INAF$High <- NULL
INAF$Low <- NULL
INAF$Adj.Close <- NULL
INAF$dret <- c(-diff(INAF$Close)/INAF$Close[-1]*100,NA)
INAF$DolValue <- INAF$Close * INAF$Volume
INAF$Liquid <- INAF$dret/INAF$DolValue
tail(INAF)INCO <- INCO[order(INCO$Date,decreasing=T),]
INCO$Open <- NULL
INCO$High <- NULL
INCO$Low <- NULL
INCO$Adj.Close <- NULL
INCO$dret <- c(-diff(INCO$Close)/INCO$Close[-1]*100,NA)
INCO$DolValue <- INCO$Close * INCO$Volume
INCO$Liquid <- INCO$dret/INCO$DolValue
tail(INCO)INDR <- INDR[order(INDR$Date,decreasing=T),]
INDR$Open <- NULL
INDR$High <- NULL
INDR$Low <- NULL
INDR$Adj.Close <- NULL
INDR$dret <- c(-diff(INDR$Close)/INDR$Close[-1]*100,NA)
INDR$DolValue <- INDR$Close * INDR$Volume
INDR$Liquid <- INDR$dret/INDR$DolValue
INDR<- do.call(data.frame,
lapply(INDR,function(x) replace(x, is.infinite(x), NA)))
tail(INDR)INDY <- INDY[order(INDY$Date,decreasing=T),]
INDY$Open <- NULL
INDY$High <- NULL
INDY$Low <- NULL
INDY$Adj.Close <- NULL
INDY$dret <- c(-diff(INDY$Close)/INDY$Close[-1]*100,NA)
INDY$DolValue <- INDY$Close * INDY$Volume
INDY$Liquid <- INDY$dret/INDY$DolValue
tail(INDY)INKP <- INKP[order(INKP$Date,decreasing=T),]
INKP$Open <- NULL
INKP$High <- NULL
INKP$Low <- NULL
INKP$Adj.Close <- NULL
INKP$dret <- c(-diff(INKP$Close)/INKP$Close[-1]*100,NA)
INKP$DolValue <- INKP$Close * INKP$Volume
INKP$Liquid <- INKP$dret/INKP$DolValue
tail(INKP)INTA <- INTA[order(INTA$Date,decreasing=T),]
INTA$Open <- NULL
INTA$High <- NULL
INTA$Low <- NULL
INTA$Adj.Close <- NULL
INTA$dret <- c(-diff(INTA$Close)/INTA$Close[-1]*100,NA)
INTA$DolValue <- INTA$Close * INTA$Volume
INTA$Liquid <- INTA$dret/INTA$DolValue
tail(INTA)JECC <- JECC[order(JECC$Date,decreasing=T),]
JECC$Open <- NULL
JECC$High <- NULL
JECC$Low <- NULL
JECC$Adj.Close <- NULL
JECC$dret <- c(-diff(JECC$Close)/JECC$Close[-1]*100,NA)
JECC$DolValue <- JECC$Close * JECC$Volume
JECC$Liquid <- JECC$dret/JECC$DolValue
tail(JECC)JRPT <- JRPT[order(JRPT$Date,decreasing=T),]
JRPT$Open <- NULL
JRPT$High <- NULL
JRPT$Low <- NULL
JRPT$Adj.Close <- NULL
JRPT$dret <- c(-diff(JRPT$Close)/JRPT$Close[-1]*100,NA)
JRPT$DolValue <- JRPT$Close * JRPT$Volume
JRPT$Liquid <- JRPT$dret/JRPT$DolValue
JRPT<- do.call(data.frame,
lapply(JRPT,function(x) replace(x, is.infinite(x), NA)))
tail(JRPT)JSMR <- JSMR[order(JSMR$Date,decreasing=T),]
JSMR$Open <- NULL
JSMR$High <- NULL
JSMR$Low <- NULL
JSMR$Adj.Close <- NULL
JSMR$dret <- c(-diff(JSMR$Close)/JSMR$Close[-1]*100,NA)
JSMR$DolValue <- JSMR$Close * JSMR$Volume
JSMR$Liquid <- JSMR$dret/JSMR$DolValue
tail(JSMR)KAEF <- KAEF[order(KAEF$Date,decreasing=T),]
KAEF$Open <- NULL
KAEF$High <- NULL
KAEF$Low <- NULL
KAEF$Adj.Close <- NULL
KAEF$dret <- c(-diff(KAEF$Close)/KAEF$Close[-1]*100,NA)
KAEF$DolValue <- KAEF$Close * KAEF$Volume
KAEF$Liquid <- KAEF$dret/KAEF$DolValue
tail(KAEF)KBLI <- KBLI[order(KBLI$Date,decreasing=T),]
KBLI$Open <- NULL
KBLI$High <- NULL
KBLI$Low <- NULL
KBLI$Adj.Close <- NULL
KBLI$dret <- c(-diff(KBLI$Close)/KBLI$Close[-1]*100,NA)
KBLI$DolValue <- KBLI$Close * KBLI$Volume
KBLI$Liquid <- KBLI$dret/KBLI$DolValue
tail(KBLI)KLBF <- KLBF[order(KLBF$Date,decreasing=T),]
KLBF$Open <- NULL
KLBF$High <- NULL
KLBF$Low <- NULL
KLBF$Adj.Close <- NULL
KLBF$dret <- c(-diff(KLBF$Close)/KLBF$Close[-1]*100,NA)
KLBF$DolValue <- KLBF$Close * KLBF$Volume
KLBF$Liquid <- KLBF$dret/KLBF$DolValue
tail(KLBF)KOIN <- KOIN[order(KOIN$Date,decreasing=T),]
KOIN$Open <- NULL
KOIN$High <- NULL
KOIN$Low <- NULL
KOIN$Adj.Close <- NULL
KOIN$dret <- c(-diff(KOIN$Close)/KOIN$Close[-1]*100,NA)
KOIN$DolValue <- KOIN$Close * KOIN$Volume
KOIN$Liquid <- KOIN$dret/KOIN$DolValue
tail(KOIN)KPIG <- KPIG[order(KPIG$Date,decreasing=T),]
KPIG$Open <- NULL
KPIG$High <- NULL
KPIG$Low <- NULL
KPIG$Adj.Close <- NULL
KPIG$dret <- c(-diff(KPIG$Close)/KPIG$Close[-1]*100,NA)
KPIG$DolValue <- KPIG$Close * KPIG$Volume
KPIG$Liquid <- KPIG$dret/KPIG$DolValue
tail(KPIG)LMPI <- LMPI[order(LMPI$Date,decreasing=T),]
LMPI$Open <- NULL
LMPI$High <- NULL
LMPI$Low <- NULL
LMPI$Adj.Close <- NULL
LMPI$dret <- c(-diff(LMPI$Close)/LMPI$Close[-1]*100,NA)
LMPI$DolValue <- LMPI$Close * LMPI$Volume
LMPI$Liquid <- LMPI$dret/LMPI$DolValue
tail(LMPI)MEDC <- MEDC[order(MEDC$Date,decreasing=T),]
MEDC$Open <- NULL
MEDC$High <- NULL
MEDC$Low <- NULL
MEDC$Adj.Close <- NULL
MEDC$dret <- c(-diff(MEDC$Close)/MEDC$Close[-1]*100,NA)
MEDC$DolValue <- MEDC$Close * MEDC$Volume
MEDC$Liquid <- MEDC$dret/MEDC$DolValue
tail(MEDC)MERK <- MERK[order(MERK$Date,decreasing=T),]
MERK$Open <- NULL
MERK$High <- NULL
MERK$Low <- NULL
MERK$Adj.Close <- NULL
MERK$dret <- c(-diff(MERK$Close)/MERK$Close[-1]*100,NA)
MERK$DolValue <- MERK$Close * MERK$Volume
MERK$Liquid <- MERK$dret/MERK$DolValue
tail(MERK)META <- META[order(META$Date,decreasing=T),]
META$Open <- NULL
META$High <- NULL
META$Low <- NULL
META$Adj.Close <- NULL
META$dret <- c(-diff(META$Close)/META$Close[-1]*100,NA)
META$DolValue <- META$Close * META$Volume
META$Liquid <- META$dret/META$DolValue
tail(META)MLBI <- MLBI[order(MLBI$Date,decreasing=T),]
MLBI$Open <- NULL
MLBI$High <- NULL
MLBI$Low <- NULL
MLBI$Adj.Close <- NULL
MLBI$dret <- c(-diff(MLBI$Close)/MLBI$Close[-1]*100,NA)
MLBI$DolValue <- MLBI$Close * MLBI$Volume
MLBI$Liquid <- MLBI$dret/MLBI$DolValue
tail(MLBI)MLIA <- MLIA[order(MLIA$Date,decreasing=T),]
MLIA$Open <- NULL
MLIA$High <- NULL
MLIA$Low <- NULL
MLIA$Adj.Close <- NULL
MLIA$dret <- c(-diff(MLIA$Close)/MLIA$Close[-1]*100,NA)
MLIA$DolValue <- MLIA$Close * MLIA$Volume
MLIA$Liquid <- MLIA$dret/MLIA$DolValue
tail(MLIA)MTDL <- MTDL[order(MTDL$Date,decreasing=T),]
MTDL$Open <- NULL
MTDL$High <- NULL
MTDL$Low <- NULL
MTDL$Adj.Close <- NULL
MTDL$dret <- c(-diff(MTDL$Close)/MTDL$Close[-1]*100,NA)
MTDL$DolValue <- MTDL$Close * MTDL$Volume
MTDL$Liquid <- MTDL$dret/MTDL$DolValue
MTDL<- do.call(data.frame,
lapply(MTDL,function(x) replace(x, is.infinite(x), NA)))
tail(MTDL)MYOH <- MYOH[order(MYOH$Date,decreasing=T),]
MYOH$Open <- NULL
MYOH$High <- NULL
MYOH$Low <- NULL
MYOH$Adj.Close <- NULL
MYOH$dret <- c(-diff(MYOH$Close)/MYOH$Close[-1]*100,NA)
MYOH$DolValue <- MYOH$Close * MYOH$Volume
MYOH$Liquid <- MYOH$dret/MYOH$DolValue
tail(MYOH)PANR <- PANR[order(PANR$Date,decreasing=T),]
PANR$Open <- NULL
PANR$High <- NULL
PANR$Low <- NULL
PANR$Adj.Close <- NULL
PANR$dret <- c(-diff(PANR$Close)/PANR$Close[-1]*100,NA)
PANR$DolValue <- PANR$Close * PANR$Volume
PANR$Liquid <- PANR$dret/PANR$DolValue
tail(PANR)PGAS <- PGAS[order(PGAS$Date,decreasing=T),]
PGAS$Open <- NULL
PGAS$High <- NULL
PGAS$Low <- NULL
PGAS$Adj.Close <- NULL
PGAS$dret <- c(-diff(PGAS$Close)/PGAS$Close[-1]*100,NA)
PGAS$DolValue <- PGAS$Close * PGAS$Volume
PGAS$Liquid <- PGAS$dret/PGAS$DolValue
tail(PGAS)PICO <- PICO[order(PICO$Date,decreasing=T),]
PICO$Open <- NULL
PICO$High <- NULL
PICO$Low <- NULL
PICO$Adj.Close <- NULL
PICO$dret <- c(-diff(PICO$Close)/PICO$Close[-1]*100,NA)
PICO$DolValue <- PICO$Close * PICO$Volume
PICO$Liquid <- PICO$dret/PICO$DolValue
PICO<- do.call(data.frame,
lapply(PICO,function(x) replace(x, is.infinite(x), NA)))
tail(PICO)PJAA <- PJAA[order(PJAA$Date,decreasing=T),]
PJAA$Open <- NULL
PJAA$High <- NULL
PJAA$Low <- NULL
PJAA$Adj.Close <- NULL
PJAA$dret <- c(-diff(PJAA$Close)/PJAA$Close[-1]*100,NA)
PJAA$DolValue <- PJAA$Close * PJAA$Volume
PJAA$Liquid <- PJAA$dret/PJAA$DolValue
tail(PJAA)PNBN <- PNBN[order(PNBN$Date,decreasing=T),]
PNBN$Open <- NULL
PNBN$High <- NULL
PNBN$Low <- NULL
PNBN$Adj.Close <- NULL
PNBN$dret <- c(-diff(PNBN$Close)/PNBN$Close[-1]*100,NA)
PNBN$DolValue <- PNBN$Close * PNBN$Volume
PNBN$Liquid <- PNBN$dret/PNBN$DolValue
tail(PNBN)PNIN <- PNIN[order(PNIN$Date,decreasing=T),]
PNIN$Open <- NULL
PNIN$High <- NULL
PNIN$Low <- NULL
PNIN$Adj.Close <- NULL
PNIN$dret <- c(-diff(PNIN$Close)/PNIN$Close[-1]*100,NA)
PNIN$DolValue <- PNIN$Close * PNIN$Volume
PNIN$Liquid <- PNIN$dret/PNIN$DolValue
tail(PNIN)PTPP <- PTPP[order(PTPP$Date,decreasing=T),]
PTPP$Open <- NULL
PTPP$High <- NULL
PTPP$Low <- NULL
PTPP$Adj.Close <- NULL
PTPP$dret <- c(-diff(PTPP$Close)/PTPP$Close[-1]*100,NA)
PTPP$DolValue <- PTPP$Close * PTPP$Volume
PTPP$Liquid <- PTPP$dret/PTPP$DolValue
tail(PTPP)PTSN <- PTSN[order(PTSN$Date,decreasing=T),]
PTSN$Open <- NULL
PTSN$High <- NULL
PTSN$Low <- NULL
PTSN$Adj.Close <- NULL
PTSN$dret <- c(-diff(PTSN$Close)/PTSN$Close[-1]*100,NA)
PTSN$DolValue <- PTSN$Close * PTSN$Volume
PTSN$Liquid <- PTSN$dret/PTSN$DolValue
PTSN<- do.call(data.frame,
lapply(PTSN,function(x) replace(x, is.infinite(x), NA)))
tail(PTSN)PWON <- PWON[order(PWON$Date,decreasing=T),]
PWON$Open <- NULL
PWON$High <- NULL
PWON$Low <- NULL
PWON$Adj.Close <- NULL
PWON$dret <- c(-diff(PWON$Close)/PWON$Close[-1]*100,NA)
PWON$DolValue <- PWON$Close * PWON$Volume
PWON$Liquid <- PWON$dret/PWON$DolValue
tail(PWON)PYFA <- PYFA[order(PYFA$Date,decreasing=T),]
PYFA$Open <- NULL
PYFA$High <- NULL
PYFA$Low <- NULL
PYFA$Adj.Close <- NULL
PYFA$dret <- c(-diff(PYFA$Close)/PYFA$Close[-1]*100,NA)
PYFA$DolValue <- PYFA$Close * PYFA$Volume
PYFA$Liquid <- PYFA$dret/PYFA$DolValue
tail(PYFA)RICY <- RICY[order(RICY$Date,decreasing=T),]
RICY$Open <- NULL
RICY$High <- NULL
RICY$Low <- NULL
RICY$Adj.Close <- NULL
RICY$dret <- c(-diff(RICY$Close)/RICY$Close[-1]*100,NA)
RICY$DolValue <- RICY$Close * RICY$Volume
RICY$Liquid <- RICY$dret/RICY$DolValue
tail(RICY)SAFE <- SAFE[order(SAFE$Date,decreasing=T),]
SAFE$Open <- NULL
SAFE$High <- NULL
SAFE$Low <- NULL
SAFE$Adj.Close <- NULL
SAFE$dret <- c(-diff(SAFE$Close)/SAFE$Close[-1]*100,NA)
SAFE$DolValue <- SAFE$Close * SAFE$Volume
SAFE$Liquid <- SAFE$dret/SAFE$DolValue
tail(SAFE)SDMU <- SDMU[order(SDMU$Date,decreasing=T),]
SDMU$Open <- NULL
SDMU$High <- NULL
SDMU$Low <- NULL
SDMU$Adj.Close <- NULL
SDMU$dret <- c(-diff(SDMU$Close)/SDMU$Close[-1]*100,NA)
SDMU$DolValue <- SDMU$Close * SDMU$Volume
SDMU$Liquid <- SDMU$dret/SDMU$DolValue
tail(SDMU)SHID <- SHID[order(SHID$Date,decreasing=T),]
SHID$Open <- NULL
SHID$High <- NULL
SHID$Low <- NULL
SHID$Adj.Close <- NULL
SHID$dret <- c(-diff(SHID$Close)/SHID$Close[-1]*100,NA)
SHID$DolValue <- SHID$Close * SHID$Volume
SHID$Liquid <- SHID$dret/SHID$DolValue
SHID<- do.call(data.frame,
lapply(SHID,function(x) replace(x, is.infinite(x), NA)))
tail(SHID)SMDM <- SMDM[order(SMDM$Date,decreasing=T),]
SMDM$Open <- NULL
SMDM$High <- NULL
SMDM$Low <- NULL
SMDM$Adj.Close <- NULL
SMDM$dret <- c(-diff(SMDM$Close)/SMDM$Close[-1]*100,NA)
SMDM$DolValue <- SMDM$Close * SMDM$Volume
SMDM$Liquid <- SMDM$dret/SMDM$DolValue
tail(SMDM)SMDR <- SMDR[order(SMDR$Date,decreasing=T),]
SMDR$Open <- NULL
SMDR$High <- NULL
SMDR$Low <- NULL
SMDR$Adj.Close <- NULL
SMDR$dret <- c(-diff(SMDR$Close)/SMDR$Close[-1]*100,NA)
SMDR$DolValue <- SMDR$Close * SMDR$Volume
SMDR$Liquid <- SMDR$dret/SMDR$DolValue
tail(SMDR)SMGR <- SMGR[order(SMGR$Date,decreasing=T),]
SMGR$Open <- NULL
SMGR$High <- NULL
SMGR$Low <- NULL
SMGR$Adj.Close <- NULL
SMGR$dret <- c(-diff(SMGR$Close)/SMGR$Close[-1]*100,NA)
SMGR$DolValue <- SMGR$Close * SMGR$Volume
SMGR$Liquid <- SMGR$dret/SMGR$DolValue
tail(SMGR)SRAJ <- SRAJ[order(SRAJ$Date,decreasing=T),]
SRAJ$Open <- NULL
SRAJ$High <- NULL
SRAJ$Low <- NULL
SRAJ$Adj.Close <- NULL
SRAJ$dret <- c(-diff(SRAJ$Close)/SRAJ$Close[-1]*100,NA)
SRAJ$DolValue <- SRAJ$Close * SRAJ$Volume
SRAJ$Liquid <- SRAJ$dret/SRAJ$DolValue
tail(SRAJ)STTP <- STTP[order(STTP$Date,decreasing=T),]
STTP$Open <- NULL
STTP$High <- NULL
STTP$Low <- NULL
STTP$Adj.Close <- NULL
STTP$dret <- c(-diff(STTP$Close)/STTP$Close[-1]*100,NA)
STTP$DolValue <- STTP$Close * STTP$Volume
STTP$Liquid <- STTP$dret/STTP$DolValue
STTP<- do.call(data.frame,
lapply(STTP,function(x) replace(x, is.infinite(x), NA)))
tail(STTP)TBLA <- TBLA[order(TBLA$Date,decreasing=T),]
TBLA$Open <- NULL
TBLA$High <- NULL
TBLA$Low <- NULL
TBLA$Adj.Close <- NULL
TBLA$dret <- c(-diff(TBLA$Close)/TBLA$Close[-1]*100,NA)
TBLA$DolValue <- TBLA$Close * TBLA$Volume
TBLA$Liquid <- TBLA$dret/TBLA$DolValue
tail(TBLA)TLKM <- TLKM[order(TLKM$Date,decreasing=T),]
TLKM$Open <- NULL
TLKM$High <- NULL
TLKM$Low <- NULL
TLKM$Adj.Close <- NULL
TLKM$dret <- c(-diff(TLKM$Close)/TLKM$Close[-1]*100,NA)
TLKM$DolValue <- TLKM$Close * TLKM$Volume
TLKM$Liquid <- TLKM$dret/TLKM$DolValue
tail(TLKM)TMAS <- TMAS[order(TMAS$Date,decreasing=T),]
TMAS$Open <- NULL
TMAS$High <- NULL
TMAS$Low <- NULL
TMAS$Adj.Close <- NULL
TMAS$dret <- c(-diff(TMAS$Close)/TMAS$Close[-1]*100,NA)
TMAS$DolValue <- TMAS$Close * TMAS$Volume
TMAS$Liquid <- TMAS$dret/TMAS$DolValue
TMAS<- do.call(data.frame,
lapply(TMAS,function(x) replace(x, is.infinite(x), NA)))
tail(TMAS)TOWR <- TOWR[order(TOWR$Date,decreasing=T),]
TOWR$Open <- NULL
TOWR$High <- NULL
TOWR$Low <- NULL
TOWR$Adj.Close <- NULL
TOWR$dret <- c(-diff(TOWR$Close)/TOWR$Close[-1]*100,NA)
TOWR$DolValue <- TOWR$Close * TOWR$Volume
TOWR$Liquid <- TOWR$dret/TOWR$DolValue
TOWR<- do.call(data.frame,
lapply(TOWR,function(x) replace(x, is.infinite(x), NA)))
tail(TOWR)TSPC <- TSPC[order(TSPC$Date,decreasing=T),]
TSPC$Open <- NULL
TSPC$High <- NULL
TSPC$Low <- NULL
TSPC$Adj.Close <- NULL
TSPC$dret <- c(-diff(TSPC$Close)/TSPC$Close[-1]*100,NA)
TSPC$DolValue <- TSPC$Close * TSPC$Volume
TSPC$Liquid <- TSPC$dret/TSPC$DolValue
tail(TSPC)UNTR <- UNTR[order(UNTR$Date,decreasing=T),]
UNTR$Open <- NULL
UNTR$High <- NULL
UNTR$Low <- NULL
UNTR$Adj.Close <- NULL
UNTR$dret <- c(-diff(UNTR$Close)/UNTR$Close[-1]*100,NA)
UNTR$DolValue <- UNTR$Close * UNTR$Volume
UNTR$Liquid <- UNTR$dret/UNTR$DolValue
tail(UNTR)UNVR <- UNVR[order(UNVR$Date,decreasing=T),]
UNVR$Open <- NULL
UNVR$High <- NULL
UNVR$Low <- NULL
UNVR$Adj.Close <- NULL
UNVR$dret <- c(-diff(UNVR$Close)/UNVR$Close[-1]*100,NA)
UNVR$DolValue <- UNVR$Close * UNVR$Volume
UNVR$Liquid <- UNVR$dret/UNVR$DolValue
tail(UNVR)WEHA <- WEHA[order(WEHA$Date,decreasing=T),]
WEHA$Open <- NULL
WEHA$High <- NULL
WEHA$Low <- NULL
WEHA$Adj.Close <- NULL
WEHA$dret <- c(-diff(WEHA$Close)/WEHA$Close[-1]*100,NA)
WEHA$DolValue <- WEHA$Close * WEHA$Volume
WEHA$Liquid <- WEHA$dret/WEHA$DolValue
tail(WEHA)WINS <- WINS[order(WINS$Date,decreasing=T),]
WINS$Open <- NULL
WINS$High <- NULL
WINS$Low <- NULL
WINS$Adj.Close <- NULL
WINS$dret <- c(-diff(WINS$Close)/WINS$Close[-1]*100,NA)
WINS$DolValue <- WINS$Close * WINS$Volume
WINS$Liquid <- WINS$dret/WINS$DolValue
tail(WINS)YPAS <- YPAS[order(YPAS$Date,decreasing=T),]
YPAS$Open <- NULL
YPAS$High <- NULL
YPAS$Low <- NULL
YPAS$Adj.Close <- NULL
YPAS$dret <- c(-diff(YPAS$Close)/YPAS$Close[-1]*100,NA)
YPAS$DolValue <- YPAS$Close * YPAS$Volume
YPAS$Liquid <- YPAS$dret/YPAS$DolValue
YPAS<- do.call(data.frame,
lapply(YPAS,function(x) replace(x, is.infinite(x), NA)))
tail(YPAS)ZBRA <- ZBRA[order(ZBRA$Date,decreasing=T),]
ZBRA$Open <- NULL
ZBRA$High <- NULL
ZBRA$Low <- NULL
ZBRA$Adj.Close <- NULL
ZBRA$dret <- c(-diff(ZBRA$Close)/ZBRA$Close[-1]*100,NA)
ZBRA$DolValue <- ZBRA$Close * ZBRA$Volume
ZBRA$Liquid <- ZBRA$dret/ZBRA$DolValue
ZBRA<- do.call(data.frame,
lapply(ZBRA,function(x) replace(x, is.infinite(x), NA)))
tail(ZBRA)Pada bagian ini, penulis menunjukkan ringkasan mengenai data Daily Return dan Liquidity yang digunakan dan dibentuk untuk keperluan korelasi. Pada bagian ini, ada beberapa hal yang ditunjukkan mengenai kedua variable tersebut: Mean: Untuk mencari rata-rata kedua variable tersebut Median: Untuk mencari nilai tengah kedua variable tersebut Standard Deviation: Untuk mencari seberapa besar perbedaan sebuah data dengan rata-rata data secara keseluruhan Max: Nilai tertinggi yang muncul dalam data Min: Nilai terendah yang muncul dalam data
Selain itu, terdapat parameter na.rm=T yang dimasukan ke dalam fungsi yang digunakan. Ini dilakukan agar proses perhitungan tidak memasukkan nilai NA dan menyebabkan error.
#Daily Return of AALI
avg_AALI_dret <- mean(AALI$dret, na.rm=T)
median_AALI_dret <- median(AALI$dret, na.rm=T)
sd_AALI_dret <- sd(AALI$dret, na.rm=T)
max_AALI_dret <- max(AALI$dret, na.rm=T)
min_AALI_dret <- min(AALI$dret, na.rm=T)
#Volume of AALI
avg_AALI_vol <- mean(AALI$Liquid, na.rm=T)
median_AALI_vol <- median(AALI$Liquid, na.rm=T)
sd_AALI_vol <- sd(AALI$Liquid, na.rm=T)
max_AALI_vol <- max(AALI$Liquid, na.rm=T)
min_AALI_vol <- min(AALI$Liquid, na.rm=T)
#Tabel untuk Return AALI
DStat_AALI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AALI_dret,median_AALI_dret,sd_AALI_dret,max_AALI_dret,min_AALI_dret)
)
DStat_AALI_dret#Tabel untuk Volume AALI
DStat_AALI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AALI_vol,median_AALI_vol,sd_AALI_vol,max_AALI_vol,min_AALI_vol)
)
DStat_AALI_vol#Daily Return of ABBA
avg_ABBA_dret <- mean(ABBA$dret, na.rm=T)
median_ABBA_dret <- median(ABBA$dret, na.rm=T)
sd_ABBA_dret <- sd(ABBA$dret, na.rm=T)
max_ABBA_dret <- max(ABBA$dret, na.rm=T)
min_ABBA_dret <- min(ABBA$dret, na.rm=T)
#Volume of ABBA
avg_ABBA_vol <- mean(ABBA$Liquid, na.rm=T)
median_ABBA_vol <- median(ABBA$Liquid, na.rm=T)
sd_ABBA_vol <- sd(ABBA$Liquid, na.rm=T)
max_ABBA_vol <- max(ABBA$Liquid, na.rm=T)
min_ABBA_vol <- min(ABBA$Liquid, na.rm=T)
#Tabel untuk Return ABBA
DStat_ABBA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ABBA_dret,median_ABBA_dret,sd_ABBA_dret,max_ABBA_dret,min_ABBA_dret)
)
DStat_ABBA_dret#Tabel untuk Volume ABBA
DStat_ABBA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ABBA_vol,median_ABBA_vol,sd_ABBA_vol,max_ABBA_vol,min_ABBA_vol)
)
DStat_ABBA_vol#Daily Return of ABMM
avg_ABMM_dret <- mean(ABMM$dret, na.rm=T)
median_ABMM_dret <- median(ABMM$dret, na.rm=T)
sd_ABMM_dret <- sd(ABMM$dret, na.rm=T)
max_ABMM_dret <- max(ABMM$dret, na.rm=T)
min_ABMM_dret <- min(ABMM$dret, na.rm=T)
#Volume of ABMM
avg_ABMM_vol <- mean(ABMM$Liquid, na.rm=T)
median_ABMM_vol <- median(ABMM$Liquid, na.rm=T)
sd_ABMM_vol <- sd(ABMM$Liquid, na.rm=T)
max_ABMM_vol <- max(ABMM$Liquid, na.rm=T)
min_ABMM_vol <- min(ABMM$Liquid, na.rm=T)
#Tabel untuk Return ABMM
DStat_ABMM_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ABMM_dret,median_ABMM_dret,sd_ABMM_dret,max_ABMM_dret,min_ABMM_dret)
)
DStat_ABMM_dret#Tabel untuk Volume ABMM
DStat_ABMM_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ABMM_vol,median_ABMM_vol,sd_ABMM_vol,max_ABMM_vol,min_ABMM_vol)
)
DStat_ABMM_vol#Daily Return of ACES
avg_ACES_dret <- mean(ACES$dret, na.rm=T)
median_ACES_dret <- median(ACES$dret, na.rm=T)
sd_ACES_dret <- sd(ACES$dret, na.rm=T)
max_ACES_dret <- max(ACES$dret, na.rm=T)
min_ACES_dret <- min(ACES$dret, na.rm=T)
#Volume of ACES
avg_ACES_vol <- mean(ACES$Liquid, na.rm=T)
median_ACES_vol <- median(ACES$Liquid, na.rm=T)
sd_ACES_vol <- sd(ACES$Liquid, na.rm=T)
max_ACES_vol <- max(ACES$Liquid, na.rm=T)
min_ACES_vol <- min(ACES$Liquid, na.rm=T)
#Tabel untuk Return ACES
DStat_ACES_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ACES_dret,median_ACES_dret,sd_ACES_dret,max_ACES_dret,min_ACES_dret)
)
DStat_ACES_dret#Tabel untuk Volume ACES
DStat_ACES_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ACES_vol,median_ACES_vol,sd_ACES_vol,max_ACES_vol,min_ACES_vol)
)
DStat_ACES_vol#Daily Return of ADES
avg_ADES_dret <- mean(ADES$dret, na.rm=T)
median_ADES_dret <- median(ADES$dret, na.rm=T)
sd_ADES_dret <- sd(ADES$dret, na.rm=T)
max_ADES_dret <- max(ADES$dret, na.rm=T)
min_ADES_dret <- min(ADES$dret, na.rm=T)
#Volume of ADES
avg_ADES_vol <- mean(ADES$Liquid, na.rm=T)
median_ADES_vol <- median(ADES$Liquid, na.rm=T)
sd_ADES_vol <- sd(ADES$Liquid, na.rm=T)
max_ADES_vol <- max(ADES$Liquid, na.rm=T)
min_ADES_vol <- min(ADES$Liquid, na.rm=T)
#Tabel untuk Return ADES
DStat_ADES_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ADES_dret,median_ADES_dret,sd_ADES_dret,max_ADES_dret,min_ADES_dret)
)
DStat_ADES_dret#Tabel untuk Volume ADES
DStat_ADES_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ADES_vol,median_ADES_vol,sd_ADES_vol,max_ADES_vol,min_ADES_vol)
)
DStat_ADES_vol#Daily Return of ADHI
avg_ADHI_dret <- mean(ADHI$dret, na.rm=T)
median_ADHI_dret <- median(ADHI$dret, na.rm=T)
sd_ADHI_dret <- sd(ADHI$dret, na.rm=T)
max_ADHI_dret <- max(ADHI$dret, na.rm=T)
min_ADHI_dret <- min(ADHI$dret, na.rm=T)
#Volume of ADHI
avg_ADHI_vol <- mean(ADHI$Liquid, na.rm=T)
median_ADHI_vol <- median(ADHI$Liquid, na.rm=T)
sd_ADHI_vol <- sd(ADHI$Liquid, na.rm=T)
max_ADHI_vol <- max(ADHI$Liquid, na.rm=T)
min_ADHI_vol <- min(ADHI$Liquid, na.rm=T)
#Tabel untuk Return ADHI
DStat_ADHI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ADHI_dret,median_ADHI_dret,sd_ADHI_dret,max_ADHI_dret,min_ADHI_dret)
)
DStat_ADHI_dret#Tabel untuk Volume ADHI
DStat_ADHI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ADHI_vol,median_ADHI_vol,sd_ADHI_vol,max_ADHI_vol,min_ADHI_vol)
)
DStat_ADHI_vol#Daily Return of ADMG
avg_ADMG_dret <- mean(ADMG$dret, na.rm=T)
median_ADMG_dret <- median(ADMG$dret, na.rm=T)
sd_ADMG_dret <- sd(ADMG$dret, na.rm=T)
max_ADMG_dret <- max(ADMG$dret, na.rm=T)
min_ADMG_dret <- min(ADMG$dret, na.rm=T)
#Volume of ADMG
avg_ADMG_vol <- mean(ADMG$Liquid, na.rm=T)
median_ADMG_vol <- median(ADMG$Liquid, na.rm=T)
sd_ADMG_vol <- sd(ADMG$Liquid, na.rm=T)
max_ADMG_vol <- max(ADMG$Liquid, na.rm=T)
min_ADMG_vol <- min(ADMG$Liquid, na.rm=T)
#Tabel untuk Return ADMG
DStat_ADMG_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ADMG_dret,median_ADMG_dret,sd_ADMG_dret,max_ADMG_dret,min_ADMG_dret)
)
DStat_ADMG_dret#Tabel untuk Volume ADMG
DStat_ADMG_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ADMG_vol,median_ADMG_vol,sd_ADMG_vol,max_ADMG_vol,min_ADMG_vol)
)
DStat_ADMG_vol#Daily Return of ADRO
avg_ADRO_dret <- mean(ADRO$dret, na.rm=T)
median_ADRO_dret <- median(ADRO$dret, na.rm=T)
sd_ADRO_dret <- sd(ADRO$dret, na.rm=T)
max_ADRO_dret <- max(ADRO$dret, na.rm=T)
min_ADRO_dret <- min(ADRO$dret, na.rm=T)
#Volume of ADRO
avg_ADRO_vol <- mean(ADRO$Liquid, na.rm=T)
median_ADRO_vol <- median(ADRO$Liquid, na.rm=T)
sd_ADRO_vol <- sd(ADRO$Liquid, na.rm=T)
max_ADRO_vol <- max(ADRO$Liquid, na.rm=T)
min_ADRO_vol <- min(ADRO$Liquid, na.rm=T)
#Tabel untuk Return ADRO
DStat_ADRO_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ADRO_dret,median_ADRO_dret,sd_ADRO_dret,max_ADRO_dret,min_ADRO_dret)
)
DStat_ADRO_dret#Tabel untuk Volume ADRO
DStat_ADRO_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ADRO_vol,median_ADRO_vol,sd_ADRO_vol,max_ADRO_vol,min_ADRO_vol)
)
DStat_ADRO_vol#Daily Return of AGRO
avg_AGRO_dret <- mean(AGRO$dret, na.rm=T)
median_AGRO_dret <- median(AGRO$dret, na.rm=T)
sd_AGRO_dret <- sd(AGRO$dret, na.rm=T)
max_AGRO_dret <- max(AGRO$dret, na.rm=T)
min_AGRO_dret <- min(AGRO$dret, na.rm=T)
#Volume of AGRO
avg_AGRO_vol <- mean(AGRO$Liquid, na.rm=T)
median_AGRO_vol <- median(AGRO$Liquid, na.rm=T)
sd_AGRO_vol <- sd(AGRO$Liquid, na.rm=T)
max_AGRO_vol <- max(AGRO$Liquid, na.rm=T)
min_AGRO_vol <- min(AGRO$Liquid, na.rm=T)
#Tabel untuk Return AGRO
DStat_AGRO_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AGRO_dret,median_AGRO_dret,sd_AGRO_dret,max_AGRO_dret,min_AGRO_dret)
)
DStat_AGRO_dret#Tabel untuk Volume AGRO
DStat_AGRO_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AGRO_vol,median_AGRO_vol,sd_AGRO_vol,max_AGRO_vol,min_AGRO_vol)
)
DStat_AGRO_vol#Daily Return of AISA
avg_AISA_dret <- mean(AISA$dret, na.rm=T)
median_AISA_dret <- median(AISA$dret, na.rm=T)
sd_AISA_dret <- sd(AISA$dret, na.rm=T)
max_AISA_dret <- max(AISA$dret, na.rm=T)
min_AISA_dret <- min(AISA$dret, na.rm=T)
#Volume of AISA
avg_AISA_vol <- mean(AISA$Liquid, na.rm=T)
median_AISA_vol <- median(AISA$Liquid, na.rm=T)
sd_AISA_vol <- sd(AISA$Liquid, na.rm=T)
max_AISA_vol <- max(AISA$Liquid, na.rm=T)
min_AISA_vol <- min(AISA$Liquid, na.rm=T)
#Tabel untuk Return AISA
DStat_AISA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AISA_dret,median_AISA_dret,sd_AISA_dret,max_AISA_dret,min_AISA_dret)
)
DStat_AISA_dret#Tabel untuk Volume AISA
DStat_AISA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AISA_vol,median_AISA_vol,sd_AISA_vol,max_AISA_vol,min_AISA_vol)
)
DStat_AISA_vol#Daily Return of AKPI
avg_AKPI_dret <- mean(AKPI$dret, na.rm=T)
median_AKPI_dret <- median(AKPI$dret, na.rm=T)
sd_AKPI_dret <- sd(AKPI$dret, na.rm=T)
max_AKPI_dret <- max(AKPI$dret, na.rm=T)
min_AKPI_dret <- min(AKPI$dret, na.rm=T)
#Volume of AKPI
avg_AKPI_vol <- mean(AKPI$Liquid, na.rm=T)
median_AKPI_vol <- median(AKPI$Liquid, na.rm=T)
sd_AKPI_vol <- sd(AKPI$Liquid, na.rm=T)
max_AKPI_vol <- max(AKPI$Liquid, na.rm=T)
min_AKPI_vol <- min(AKPI$Liquid, na.rm=T)
#Tabel untuk Return AKPI
DStat_AKPI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AKPI_dret,median_AKPI_dret,sd_AKPI_dret,max_AKPI_dret,min_AKPI_dret)
)
DStat_AKPI_dret#Tabel untuk Volume AKPI
DStat_AKPI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AKPI_vol,median_AKPI_vol,sd_AKPI_vol,max_AKPI_vol,min_AKPI_vol)
)
DStat_AKPI_vol#Daily Return of AKRA
avg_AKRA_dret <- mean(AKRA$dret, na.rm=T)
median_AKRA_dret <- median(AKRA$dret, na.rm=T)
sd_AKRA_dret <- sd(AKRA$dret, na.rm=T)
max_AKRA_dret <- max(AKRA$dret, na.rm=T)
min_AKRA_dret <- min(AKRA$dret, na.rm=T)
#Volume of AKRA
avg_AKRA_vol <- mean(AKRA$Liquid, na.rm=T)
median_AKRA_vol <- median(AKRA$Liquid, na.rm=T)
sd_AKRA_vol <- sd(AKRA$Liquid, na.rm=T)
max_AKRA_vol <- max(AKRA$Liquid, na.rm=T)
min_AKRA_vol <- min(AKRA$Liquid, na.rm=T)
#Tabel untuk Return AKRA
DStat_AKRA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AKRA_dret,median_AKRA_dret,sd_AKRA_dret,max_AKRA_dret,min_AKRA_dret)
)
DStat_AKRA_dret#Tabel untuk Volume AKRA
DStat_AKRA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AKRA_vol,median_AKRA_vol,sd_AKRA_vol,max_AKRA_vol,min_AKRA_vol)
)
DStat_AKRA_vol#Daily Return of AKSI
avg_AKSI_dret <- mean(AKSI$dret, na.rm=T)
median_AKSI_dret <- median(AKSI$dret, na.rm=T)
sd_AKSI_dret <- sd(AKSI$dret, na.rm=T)
max_AKSI_dret <- max(AKSI$dret, na.rm=T)
min_AKSI_dret <- min(AKSI$dret, na.rm=T)
#Volume of AKSI
avg_AKSI_vol <- mean(AKSI$Liquid, na.rm=T)
median_AKSI_vol <- median(AKSI$Liquid, na.rm=T)
sd_AKSI_vol <- sd(AKSI$Liquid, na.rm=T)
max_AKSI_vol <- max(AKSI$Liquid, na.rm=T)
min_AKSI_vol <- min(AKSI$Liquid, na.rm=T)
#Tabel untuk Return AKSI
DStat_AKSI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AKSI_dret,median_AKSI_dret,sd_AKSI_dret,max_AKSI_dret,min_AKSI_dret)
)
DStat_AKSI_dret#Tabel untuk Volume AKSI
DStat_AKSI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AKSI_vol,median_AKSI_vol,sd_AKSI_vol,max_AKSI_vol,min_AKSI_vol)
)
DStat_AKSI_vol#Daily Return of ALDO
avg_ALDO_dret <- mean(ALDO$dret, na.rm=T)
median_ALDO_dret <- median(ALDO$dret, na.rm=T)
sd_ALDO_dret <- sd(ALDO$dret, na.rm=T)
max_ALDO_dret <- max(ALDO$dret, na.rm=T)
min_ALDO_dret <- min(ALDO$dret, na.rm=T)
#Volume of ALDO
avg_ALDO_vol <- mean(ALDO$Liquid, na.rm=T)
median_ALDO_vol <- median(ALDO$Liquid, na.rm=T)
sd_ALDO_vol <- sd(ALDO$Liquid, na.rm=T)
max_ALDO_vol <- max(ALDO$Liquid, na.rm=T)
min_ALDO_vol <- min(ALDO$Liquid, na.rm=T)
#Tabel untuk Return ALDO
DStat_ALDO_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ALDO_dret,median_ALDO_dret,sd_ALDO_dret,max_ALDO_dret,min_ALDO_dret)
)
DStat_ALDO_dret#Tabel untuk Volume ALDO
DStat_ALDO_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ALDO_vol,median_ALDO_vol,sd_ALDO_vol,max_ALDO_vol,min_ALDO_vol)
)
DStat_ALDO_vol#Daily Return of ALMI
avg_ALMI_dret <- mean(ALMI$dret, na.rm=T)
median_ALMI_dret <- median(ALMI$dret, na.rm=T)
sd_ALMI_dret <- sd(ALMI$dret, na.rm=T)
max_ALMI_dret <- max(ALMI$dret, na.rm=T)
min_ALMI_dret <- min(ALMI$dret, na.rm=T)
#Volume of ALMI
avg_ALMI_vol <- mean(ALMI$Liquid, na.rm=T)
median_ALMI_vol <- median(ALMI$Liquid, na.rm=T)
sd_ALMI_vol <- sd(ALMI$Liquid, na.rm=T)
max_ALMI_vol <- max(ALMI$Liquid, na.rm=T)
min_ALMI_vol <- min(ALMI$Liquid, na.rm=T)
#Tabel untuk Return ALMI
DStat_ALMI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ALMI_dret,median_ALMI_dret,sd_ALMI_dret,max_ALMI_dret,min_ALMI_dret)
)
DStat_ALMI_dret#Tabel untuk Volume ALMI
DStat_ALMI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ALMI_vol,median_ALMI_vol,sd_ALMI_vol,max_ALMI_vol,min_ALMI_vol)
)
DStat_ALMI_vol#Daily Return of AMFG
avg_AMFG_dret <- mean(AMFG$dret, na.rm=T)
median_AMFG_dret <- median(AMFG$dret, na.rm=T)
sd_AMFG_dret <- sd(AMFG$dret, na.rm=T)
max_AMFG_dret <- max(AMFG$dret, na.rm=T)
min_AMFG_dret <- min(AMFG$dret, na.rm=T)
#Volume of AMFG
avg_AMFG_vol <- mean(AMFG$Liquid, na.rm=T)
median_AMFG_vol <- median(AMFG$Liquid, na.rm=T)
sd_AMFG_vol <- sd(AMFG$Liquid, na.rm=T)
max_AMFG_vol <- max(AMFG$Liquid, na.rm=T)
min_AMFG_vol <- min(AMFG$Liquid, na.rm=T)
#Tabel untuk Return AMFG
DStat_AMFG_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AMFG_dret,median_AMFG_dret,sd_AMFG_dret,max_AMFG_dret,min_AMFG_dret)
)
DStat_AMFG_dret#Tabel untuk Volume AMFG
DStat_AMFG_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AMFG_vol,median_AMFG_vol,sd_AMFG_vol,max_AMFG_vol,min_AMFG_vol)
)
DStat_AMFG_vol#Daily Return of AMRT
avg_AMRT_dret <- mean(AMRT$dret, na.rm=T)
median_AMRT_dret <- median(AMRT$dret, na.rm=T)
sd_AMRT_dret <- sd(AMRT$dret, na.rm=T)
max_AMRT_dret <- max(AMRT$dret, na.rm=T)
min_AMRT_dret <- min(AMRT$dret, na.rm=T)
#Volume of AMRT
avg_AMRT_vol <- mean(AMRT$Liquid, na.rm=T)
median_AMRT_vol <- median(AMRT$Liquid, na.rm=T)
sd_AMRT_vol <- sd(AMRT$Liquid, na.rm=T)
max_AMRT_vol <- max(AMRT$Liquid, na.rm=T)
min_AMRT_vol <- min(AMRT$Liquid, na.rm=T)
#Tabel untuk Return AMRT
DStat_AMRT_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AMRT_dret,median_AMRT_dret,sd_AMRT_dret,max_AMRT_dret,min_AMRT_dret)
)
DStat_AMRT_dret#Tabel untuk Volume AMRT
DStat_AMRT_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AMRT_vol,median_AMRT_vol,sd_AMRT_vol,max_AMRT_vol,min_AMRT_vol)
)
DStat_AMRT_vol#Daily Return of ANTM
avg_ANTM_dret <- mean(ANTM$dret, na.rm=T)
median_ANTM_dret <- median(ANTM$dret, na.rm=T)
sd_ANTM_dret <- sd(ANTM$dret, na.rm=T)
max_ANTM_dret <- max(ANTM$dret, na.rm=T)
min_ANTM_dret <- min(ANTM$dret, na.rm=T)
#Volume of ANTM
avg_ANTM_vol <- mean(ANTM$Liquid, na.rm=T)
median_ANTM_vol <- median(ANTM$Liquid, na.rm=T)
sd_ANTM_vol <- sd(ANTM$Liquid, na.rm=T)
max_ANTM_vol <- max(ANTM$Liquid, na.rm=T)
min_ANTM_vol <- min(ANTM$Liquid, na.rm=T)
#Tabel untuk Return ANTM
DStat_ANTM_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ANTM_dret,median_ANTM_dret,sd_ANTM_dret,max_ANTM_dret,min_ANTM_dret)
)
DStat_ANTM_dret#Tabel untuk Volume ANTM
DStat_ANTM_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ANTM_vol,median_ANTM_vol,sd_ANTM_vol,max_ANTM_vol,min_ANTM_vol)
)
DStat_ANTM_vol#Daily Return of APLN
avg_APLN_dret <- mean(APLN$dret, na.rm=T)
median_APLN_dret <- median(APLN$dret, na.rm=T)
sd_APLN_dret <- sd(APLN$dret, na.rm=T)
max_APLN_dret <- max(APLN$dret, na.rm=T)
min_APLN_dret <- min(APLN$dret, na.rm=T)
#Volume of APLN
avg_APLN_vol <- mean(APLN$Liquid, na.rm=T)
median_APLN_vol <- median(APLN$Liquid, na.rm=T)
sd_APLN_vol <- sd(APLN$Liquid, na.rm=T)
max_APLN_vol <- max(APLN$Liquid, na.rm=T)
min_APLN_vol <- min(APLN$Liquid, na.rm=T)
#Tabel untuk Return APLN
DStat_APLN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_APLN_dret,median_APLN_dret,sd_APLN_dret,max_APLN_dret,min_APLN_dret)
)
DStat_APLN_dret#Tabel untuk Volume APLN
DStat_APLN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_APLN_vol,median_APLN_vol,sd_APLN_vol,max_APLN_vol,min_APLN_vol)
)
DStat_APLN_vol#Daily Return of ASII
avg_ASII_dret <- mean(ASII$dret, na.rm=T)
median_ASII_dret <- median(ASII$dret, na.rm=T)
sd_ASII_dret <- sd(ASII$dret, na.rm=T)
max_ASII_dret <- max(ASII$dret, na.rm=T)
min_ASII_dret <- min(ASII$dret, na.rm=T)
#Volume of ASII
avg_ASII_vol <- mean(ASII$Liquid, na.rm=T)
median_ASII_vol <- median(ASII$Liquid, na.rm=T)
sd_ASII_vol <- sd(ASII$Liquid, na.rm=T)
max_ASII_vol <- max(ASII$Liquid, na.rm=T)
min_ASII_vol <- min(ASII$Liquid, na.rm=T)
#Tabel untuk Return ASII
DStat_ASII_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ASII_dret,median_ASII_dret,sd_ASII_dret,max_ASII_dret,min_ASII_dret)
)
DStat_ASII_dret#Tabel untuk Volume ASII
DStat_ASII_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ASII_vol,median_ASII_vol,sd_ASII_vol,max_ASII_vol,min_ASII_vol)
)
DStat_ASII_vol#Daily Return of ASRI
avg_ASRI_dret <- mean(ASRI$dret, na.rm=T)
median_ASRI_dret <- median(ASRI$dret, na.rm=T)
sd_ASRI_dret <- sd(ASRI$dret, na.rm=T)
max_ASRI_dret <- max(ASRI$dret, na.rm=T)
min_ASRI_dret <- min(ASRI$dret, na.rm=T)
#Volume of ASRI
avg_ASRI_vol <- mean(ASRI$Liquid, na.rm=T)
median_ASRI_vol <- median(ASRI$Liquid, na.rm=T)
sd_ASRI_vol <- sd(ASRI$Liquid, na.rm=T)
max_ASRI_vol <- max(ASRI$Liquid, na.rm=T)
min_ASRI_vol <- min(ASRI$Liquid, na.rm=T)
#Tabel untuk Return ASRI
DStat_ASRI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ASRI_dret,median_ASRI_dret,sd_ASRI_dret,max_ASRI_dret,min_ASRI_dret)
)
DStat_ASRI_dret#Tabel untuk Volume ASRI
DStat_ASRI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ASRI_vol,median_ASRI_vol,sd_ASRI_vol,max_ASRI_vol,min_ASRI_vol)
)
DStat_ASRI_vol#Daily Return of AUTO
avg_AUTO_dret <- mean(AUTO$dret, na.rm=T)
median_AUTO_dret <- median(AUTO$dret, na.rm=T)
sd_AUTO_dret <- sd(AUTO$dret, na.rm=T)
max_AUTO_dret <- max(AUTO$dret, na.rm=T)
min_AUTO_dret <- min(AUTO$dret, na.rm=T)
#Volume of AUTO
avg_AUTO_vol <- mean(AUTO$Liquid, na.rm=T)
median_AUTO_vol <- median(AUTO$Liquid, na.rm=T)
sd_AUTO_vol <- sd(AUTO$Liquid, na.rm=T)
max_AUTO_vol <- max(AUTO$Liquid, na.rm=T)
min_AUTO_vol <- min(AUTO$Liquid, na.rm=T)
#Tabel untuk Return AUTO
DStat_AUTO_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_AUTO_dret,median_AUTO_dret,sd_AUTO_dret,max_AUTO_dret,min_AUTO_dret)
)
DStat_AUTO_dret#Tabel untuk Volume AUTO
DStat_AUTO_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_AUTO_vol,median_AUTO_vol,sd_AUTO_vol,max_AUTO_vol,min_AUTO_vol)
)
DStat_AUTO_vol#Daily Return of BABP
avg_BABP_dret <- mean(BABP$dret, na.rm=T)
median_BABP_dret <- median(BABP$dret, na.rm=T)
sd_BABP_dret <- sd(BABP$dret, na.rm=T)
max_BABP_dret <- max(BABP$dret, na.rm=T)
min_BABP_dret <- min(BABP$dret, na.rm=T)
#Volume of BABP
avg_BABP_vol <- mean(BABP$Liquid, na.rm=T)
median_BABP_vol <- median(BABP$Liquid, na.rm=T)
sd_BABP_vol <- sd(BABP$Liquid, na.rm=T)
max_BABP_vol <- max(BABP$Liquid, na.rm=T)
min_BABP_vol <- min(BABP$Liquid, na.rm=T)
#Tabel untuk Return BABP
DStat_BABP_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BABP_dret,median_BABP_dret,sd_BABP_dret,max_BABP_dret,min_BABP_dret)
)
DStat_BABP_dret#Tabel untuk Volume BABP
DStat_BABP_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BABP_vol,median_BABP_vol,sd_BABP_vol,max_BABP_vol,min_BABP_vol)
)
DStat_BABP_vol#Daily Return of BAPA
avg_BAPA_dret <- mean(BAPA$dret, na.rm=T)
median_BAPA_dret <- median(BAPA$dret, na.rm=T)
sd_BAPA_dret <- sd(BAPA$dret, na.rm=T)
max_BAPA_dret <- max(BAPA$dret, na.rm=T)
min_BAPA_dret <- min(BAPA$dret, na.rm=T)
#Volume of BAPA
avg_BAPA_vol <- mean(BAPA$Liquid, na.rm=T)
median_BAPA_vol <- median(BAPA$Liquid, na.rm=T)
sd_BAPA_vol <- sd(BAPA$Liquid, na.rm=T)
max_BAPA_vol <- max(BAPA$Liquid, na.rm=T)
min_BAPA_vol <- min(BAPA$Liquid, na.rm=T)
#Tabel untuk Return BAPA
DStat_BAPA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BAPA_dret,median_BAPA_dret,sd_BAPA_dret,max_BAPA_dret,min_BAPA_dret)
)
DStat_BAPA_dret#Tabel untuk Volume BAPA
DStat_BAPA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BAPA_vol,median_BAPA_vol,sd_BAPA_vol,max_BAPA_vol,min_BAPA_vol)
)
DStat_BAPA_vol#Daily Return of BAYU
avg_BAYU_dret <- mean(BAYU$dret, na.rm=T)
median_BAYU_dret <- median(BAYU$dret, na.rm=T)
sd_BAYU_dret <- sd(BAYU$dret, na.rm=T)
max_BAYU_dret <- max(BAYU$dret, na.rm=T)
min_BAYU_dret <- min(BAYU$dret, na.rm=T)
#Volume of BAYU
avg_BAYU_vol <- mean(BAYU$Liquid, na.rm=T)
median_BAYU_vol <- median(BAYU$Liquid, na.rm=T)
sd_BAYU_vol <- sd(BAYU$Liquid, na.rm=T)
max_BAYU_vol <- max(BAYU$Liquid, na.rm=T)
min_BAYU_vol <- min(BAYU$Liquid, na.rm=T)
#Tabel untuk Return BAYU
DStat_BAYU_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BAYU_dret,median_BAYU_dret,sd_BAYU_dret,max_BAYU_dret,min_BAYU_dret)
)
DStat_BAYU_dret#Tabel untuk Volume BAYU
DStat_BAYU_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BAYU_vol,median_BAYU_vol,sd_BAYU_vol,max_BAYU_vol,min_BAYU_vol)
)
DStat_BAYU_vol#Daily Return of BBCA
avg_BBCA_dret <- mean(BBCA$dret, na.rm=T)
median_BBCA_dret <- median(BBCA$dret, na.rm=T)
sd_BBCA_dret <- sd(BBCA$dret, na.rm=T)
max_BBCA_dret <- max(BBCA$dret, na.rm=T)
min_BBCA_dret <- min(BBCA$dret, na.rm=T)
#Volume of BBCA
avg_BBCA_vol <- mean(BBCA$Liquid, na.rm=T)
median_BBCA_vol <- median(BBCA$Liquid, na.rm=T)
sd_BBCA_vol <- sd(BBCA$Liquid, na.rm=T)
max_BBCA_vol <- max(BBCA$Liquid, na.rm=T)
min_BBCA_vol <- min(BBCA$Liquid, na.rm=T)
#Tabel untuk Return BBCA
DStat_BBCA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BBCA_dret,median_BBCA_dret,sd_BBCA_dret,max_BBCA_dret,min_BBCA_dret)
)
DStat_BBCA_dret#Tabel untuk Volume BBCA
DStat_BBCA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BBCA_vol,median_BBCA_vol,sd_BBCA_vol,max_BBCA_vol,min_BBCA_vol)
)
DStat_BBCA_vol#Daily Return of BBRI
avg_BBRI_dret <- mean(BBRI$dret, na.rm=T)
median_BBRI_dret <- median(BBRI$dret, na.rm=T)
sd_BBRI_dret <- sd(BBRI$dret, na.rm=T)
max_BBRI_dret <- max(BBRI$dret, na.rm=T)
min_BBRI_dret <- min(BBRI$dret, na.rm=T)
#Volume of BBRI
avg_BBRI_vol <- mean(BBRI$Liquid, na.rm=T)
median_BBRI_vol <- median(BBRI$Liquid, na.rm=T)
sd_BBRI_vol <- sd(BBRI$Liquid, na.rm=T)
max_BBRI_vol <- max(BBRI$Liquid, na.rm=T)
min_BBRI_vol <- min(BBRI$Liquid, na.rm=T)
#Tabel untuk Return BBRI
DStat_BBRI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BBRI_dret,median_BBRI_dret,sd_BBRI_dret,max_BBRI_dret,min_BBRI_dret)
)
DStat_BBRI_dret#Tabel untuk Volume BBRI
DStat_BBRI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BBRI_vol,median_BBRI_vol,sd_BBRI_vol,max_BBRI_vol,min_BBRI_vol)
)
DStat_BBRI_vol#Daily Return of BBTN
avg_BBTN_dret <- mean(BBTN$dret, na.rm=T)
median_BBTN_dret <- median(BBTN$dret, na.rm=T)
sd_BBTN_dret <- sd(BBTN$dret, na.rm=T)
max_BBTN_dret <- max(BBTN$dret, na.rm=T)
min_BBTN_dret <- min(BBTN$dret, na.rm=T)
#Volume of BBTN
avg_BBTN_vol <- mean(BBTN$Liquid, na.rm=T)
median_BBTN_vol <- median(BBTN$Liquid, na.rm=T)
sd_BBTN_vol <- sd(BBTN$Liquid, na.rm=T)
max_BBTN_vol <- max(BBTN$Liquid, na.rm=T)
min_BBTN_vol <- min(BBTN$Liquid, na.rm=T)
#Tabel untuk Return BBTN
DStat_BBTN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BBTN_dret,median_BBTN_dret,sd_BBTN_dret,max_BBTN_dret,min_BBTN_dret)
)
DStat_BBTN_dret#Tabel untuk Volume BBTN
DStat_BBTN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BBTN_vol,median_BBTN_vol,sd_BBTN_vol,max_BBTN_vol,min_BBTN_vol)
)
DStat_BBTN_vol#Daily Return of BDMN
avg_BDMN_dret <- mean(BDMN$dret, na.rm=T)
median_BDMN_dret <- median(BDMN$dret, na.rm=T)
sd_BDMN_dret <- sd(BDMN$dret, na.rm=T)
max_BDMN_dret <- max(BDMN$dret, na.rm=T)
min_BDMN_dret <- min(BDMN$dret, na.rm=T)
#Volume of BDMN
avg_BDMN_vol <- mean(BDMN$Liquid, na.rm=T)
median_BDMN_vol <- median(BDMN$Liquid, na.rm=T)
sd_BDMN_vol <- sd(BDMN$Liquid, na.rm=T)
max_BDMN_vol <- max(BDMN$Liquid, na.rm=T)
min_BDMN_vol <- min(BDMN$Liquid, na.rm=T)
#Tabel untuk Return BDMN
DStat_BDMN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BDMN_dret,median_BDMN_dret,sd_BDMN_dret,max_BDMN_dret,min_BDMN_dret)
)
DStat_BDMN_dret#Tabel untuk Volume BDMN
DStat_BDMN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BDMN_vol,median_BDMN_vol,sd_BDMN_vol,max_BDMN_vol,min_BDMN_vol)
)
DStat_BDMN_vol#Daily Return of BFIN
avg_BFIN_dret <- mean(BFIN$dret, na.rm=T)
median_BFIN_dret <- median(BFIN$dret, na.rm=T)
sd_BFIN_dret <- sd(BFIN$dret, na.rm=T)
max_BFIN_dret <- max(BFIN$dret, na.rm=T)
min_BFIN_dret <- min(BFIN$dret, na.rm=T)
#Volume of BFIN
avg_BFIN_vol <- mean(BFIN$Liquid, na.rm=T)
median_BFIN_vol <- median(BFIN$Liquid, na.rm=T)
sd_BFIN_vol <- sd(BFIN$Liquid, na.rm=T)
max_BFIN_vol <- max(BFIN$Liquid, na.rm=T)
min_BFIN_vol <- min(BFIN$Liquid, na.rm=T)
#Tabel untuk Return BFIN
DStat_BFIN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BFIN_dret,median_BFIN_dret,sd_BFIN_dret,max_BFIN_dret,min_BFIN_dret)
)
DStat_BFIN_dret#Tabel untuk Volume BFIN
DStat_BFIN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BFIN_vol,median_BFIN_vol,sd_BFIN_vol,max_BFIN_vol,min_BFIN_vol)
)
DStat_BFIN_vol#Daily Return of BHIT
avg_BHIT_dret <- mean(BHIT$dret, na.rm=T)
median_BHIT_dret <- median(BHIT$dret, na.rm=T)
sd_BHIT_dret <- sd(BHIT$dret, na.rm=T)
max_BHIT_dret <- max(BHIT$dret, na.rm=T)
min_BHIT_dret <- min(BHIT$dret, na.rm=T)
#Volume of BHIT
avg_BHIT_vol <- mean(BHIT$Liquid, na.rm=T)
median_BHIT_vol <- median(BHIT$Liquid, na.rm=T)
sd_BHIT_vol <- sd(BHIT$Liquid, na.rm=T)
max_BHIT_vol <- max(BHIT$Liquid, na.rm=T)
min_BHIT_vol <- min(BHIT$Liquid, na.rm=T)
#Tabel untuk Return BHIT
DStat_BHIT_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BHIT_dret,median_BHIT_dret,sd_BHIT_dret,max_BHIT_dret,min_BHIT_dret)
)
DStat_BHIT_dret#Tabel untuk Volume BHIT
DStat_BHIT_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BHIT_vol,median_BHIT_vol,sd_BHIT_vol,max_BHIT_vol,min_BHIT_vol)
)
DStat_BHIT_vol#Daily Return of BIPP
avg_BIPP_dret <- mean(BIPP$dret, na.rm=T)
median_BIPP_dret <- median(BIPP$dret, na.rm=T)
sd_BIPP_dret <- sd(BIPP$dret, na.rm=T)
max_BIPP_dret <- max(BIPP$dret, na.rm=T)
min_BIPP_dret <- min(BIPP$dret, na.rm=T)
#Volume of BIPP
avg_BIPP_vol <- mean(BIPP$Liquid, na.rm=T)
median_BIPP_vol <- median(BIPP$Liquid, na.rm=T)
sd_BIPP_vol <- sd(BIPP$Liquid, na.rm=T)
max_BIPP_vol <- max(BIPP$Liquid, na.rm=T)
min_BIPP_vol <- min(BIPP$Liquid, na.rm=T)
#Tabel untuk Return BIPP
DStat_BIPP_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BIPP_dret,median_BIPP_dret,sd_BIPP_dret,max_BIPP_dret,min_BIPP_dret)
)
DStat_BIPP_dret#Tabel untuk Volume BIPP
DStat_BIPP_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BIPP_vol,median_BIPP_vol,sd_BIPP_vol,max_BIPP_vol,min_BIPP_vol)
)
DStat_BIPP_vol#Daily Return of BISI
avg_BISI_dret <- mean(BISI$dret, na.rm=T)
median_BISI_dret <- median(BISI$dret, na.rm=T)
sd_BISI_dret <- sd(BISI$dret, na.rm=T)
max_BISI_dret <- max(BISI$dret, na.rm=T)
min_BISI_dret <- min(BISI$dret, na.rm=T)
#Volume of BISI
avg_BISI_vol <- mean(BISI$Liquid, na.rm=T)
median_BISI_vol <- median(BISI$Liquid, na.rm=T)
sd_BISI_vol <- sd(BISI$Liquid, na.rm=T)
max_BISI_vol <- max(BISI$Liquid, na.rm=T)
min_BISI_vol <- min(BISI$Liquid, na.rm=T)
#Tabel untuk Return BISI
DStat_BISI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BISI_dret,median_BISI_dret,sd_BISI_dret,max_BISI_dret,min_BISI_dret)
)
DStat_BISI_dret#Tabel untuk Volume BISI
DStat_BISI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BISI_vol,median_BISI_vol,sd_BISI_vol,max_BISI_vol,min_BISI_vol)
)
DStat_BISI_vol#Daily Return of BKDP
avg_BKDP_dret <- mean(BKDP$dret, na.rm=T)
median_BKDP_dret <- median(BKDP$dret, na.rm=T)
sd_BKDP_dret <- sd(BKDP$dret, na.rm=T)
max_BKDP_dret <- max(BKDP$dret, na.rm=T)
min_BKDP_dret <- min(BKDP$dret, na.rm=T)
#Volume of BKDP
avg_BKDP_vol <- mean(BKDP$Liquid, na.rm=T)
median_BKDP_vol <- median(BKDP$Liquid, na.rm=T)
sd_BKDP_vol <- sd(BKDP$Liquid, na.rm=T)
max_BKDP_vol <- max(BKDP$Liquid, na.rm=T)
min_BKDP_vol <- min(BKDP$Liquid, na.rm=T)
#Tabel untuk Return BKDP
DStat_BKDP_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BKDP_dret,median_BKDP_dret,sd_BKDP_dret,max_BKDP_dret,min_BKDP_dret)
)
DStat_BKDP_dret#Tabel untuk Volume BKDP
DStat_BKDP_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BKDP_vol,median_BKDP_vol,sd_BKDP_vol,max_BKDP_vol,min_BKDP_vol)
)
DStat_BKDP_vol#Daily Return of BKSL
avg_BKSL_dret <- mean(BKSL$dret, na.rm=T)
median_BKSL_dret <- median(BKSL$dret, na.rm=T)
sd_BKSL_dret <- sd(BKSL$dret, na.rm=T)
max_BKSL_dret <- max(BKSL$dret, na.rm=T)
min_BKSL_dret <- min(BKSL$dret, na.rm=T)
#Volume of BKSL
avg_BKSL_vol <- mean(BKSL$Liquid, na.rm=T)
median_BKSL_vol <- median(BKSL$Liquid, na.rm=T)
sd_BKSL_vol <- sd(BKSL$Liquid, na.rm=T)
max_BKSL_vol <- max(BKSL$Liquid, na.rm=T)
min_BKSL_vol <- min(BKSL$Liquid, na.rm=T)
#Tabel untuk Return BKSL
DStat_BKSL_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BKSL_dret,median_BKSL_dret,sd_BKSL_dret,max_BKSL_dret,min_BKSL_dret)
)
DStat_BKSL_dret#Tabel untuk Volume BKSL
DStat_BKSL_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BKSL_vol,median_BKSL_vol,sd_BKSL_vol,max_BKSL_vol,min_BKSL_vol)
)
DStat_BKSL_vol#Daily Return of BMRI
avg_BMRI_dret <- mean(BMRI$dret, na.rm=T)
median_BMRI_dret <- median(BMRI$dret, na.rm=T)
sd_BMRI_dret <- sd(BMRI$dret, na.rm=T)
max_BMRI_dret <- max(BMRI$dret, na.rm=T)
min_BMRI_dret <- min(BMRI$dret, na.rm=T)
#Volume of BMRI
avg_BMRI_vol <- mean(BMRI$Liquid, na.rm=T)
median_BMRI_vol <- median(BMRI$Liquid, na.rm=T)
sd_BMRI_vol <- sd(BMRI$Liquid, na.rm=T)
max_BMRI_vol <- max(BMRI$Liquid, na.rm=T)
min_BMRI_vol <- min(BMRI$Liquid, na.rm=T)
#Tabel untuk Return BMRI
DStat_BMRI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BMRI_dret,median_BMRI_dret,sd_BMRI_dret,max_BMRI_dret,min_BMRI_dret)
)
DStat_BMRI_dret#Tabel untuk Volume BMRI
DStat_BMRI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BMRI_vol,median_BMRI_vol,sd_BMRI_vol,max_BMRI_vol,min_BMRI_vol)
)
DStat_BMRI_vol#Daily Return of BMTR
avg_BMTR_dret <- mean(BMTR$dret, na.rm=T)
median_BMTR_dret <- median(BMTR$dret, na.rm=T)
sd_BMTR_dret <- sd(BMTR$dret, na.rm=T)
max_BMTR_dret <- max(BMTR$dret, na.rm=T)
min_BMTR_dret <- min(BMTR$dret, na.rm=T)
#Volume of BMTR
avg_BMTR_vol <- mean(BMTR$Liquid, na.rm=T)
median_BMTR_vol <- median(BMTR$Liquid, na.rm=T)
sd_BMTR_vol <- sd(BMTR$Liquid, na.rm=T)
max_BMTR_vol <- max(BMTR$Liquid, na.rm=T)
min_BMTR_vol <- min(BMTR$Liquid, na.rm=T)
#Tabel untuk Return BMTR
DStat_BMTR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BMTR_dret,median_BMTR_dret,sd_BMTR_dret,max_BMTR_dret,min_BMTR_dret)
)
DStat_BMTR_dret#Tabel untuk Volume BMTR
DStat_BMTR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BMTR_vol,median_BMTR_vol,sd_BMTR_vol,max_BMTR_vol,min_BMTR_vol)
)
DStat_BMTR_vol#Daily Return of BNBA
avg_BNBA_dret <- mean(BNBA$dret, na.rm=T)
median_BNBA_dret <- median(BNBA$dret, na.rm=T)
sd_BNBA_dret <- sd(BNBA$dret, na.rm=T)
max_BNBA_dret <- max(BNBA$dret, na.rm=T)
min_BNBA_dret <- min(BNBA$dret, na.rm=T)
#Volume of BNBA
avg_BNBA_vol <- mean(BNBA$Liquid, na.rm=T)
median_BNBA_vol <- median(BNBA$Liquid, na.rm=T)
sd_BNBA_vol <- sd(BNBA$Liquid, na.rm=T)
max_BNBA_vol <- max(BNBA$Liquid, na.rm=T)
min_BNBA_vol <- min(BNBA$Liquid, na.rm=T)
#Tabel untuk Return BNBA
DStat_BNBA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BNBA_dret,median_BNBA_dret,sd_BNBA_dret,max_BNBA_dret,min_BNBA_dret)
)
DStat_BNBA_dret#Tabel untuk Volume BNBA
DStat_BNBA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BNBA_vol,median_BNBA_vol,sd_BNBA_vol,max_BNBA_vol,min_BNBA_vol)
)
DStat_BNBA_vol#Daily Return of BNGA
avg_BNGA_dret <- mean(BNGA$dret, na.rm=T)
median_BNGA_dret <- median(BNGA$dret, na.rm=T)
sd_BNGA_dret <- sd(BNGA$dret, na.rm=T)
max_BNGA_dret <- max(BNGA$dret, na.rm=T)
min_BNGA_dret <- min(BNGA$dret, na.rm=T)
#Volume of BNGA
avg_BNGA_vol <- mean(BNGA$Liquid, na.rm=T)
median_BNGA_vol <- median(BNGA$Liquid, na.rm=T)
sd_BNGA_vol <- sd(BNGA$Liquid, na.rm=T)
max_BNGA_vol <- max(BNGA$Liquid, na.rm=T)
min_BNGA_vol <- min(BNGA$Liquid, na.rm=T)
#Tabel untuk Return BNGA
DStat_BNGA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BNGA_dret,median_BNGA_dret,sd_BNGA_dret,max_BNGA_dret,min_BNGA_dret)
)
DStat_BNGA_dret#Tabel untuk Volume BNGA
DStat_BNGA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BNGA_vol,median_BNGA_vol,sd_BNGA_vol,max_BNGA_vol,min_BNGA_vol)
)
DStat_BNGA_vol#Daily Return of BSDE
avg_BSDE_dret <- mean(BSDE$dret, na.rm=T)
median_BSDE_dret <- median(BSDE$dret, na.rm=T)
sd_BSDE_dret <- sd(BSDE$dret, na.rm=T)
max_BSDE_dret <- max(BSDE$dret, na.rm=T)
min_BSDE_dret <- min(BSDE$dret, na.rm=T)
#Volume of BSDE
avg_BSDE_vol <- mean(BSDE$Liquid, na.rm=T)
median_BSDE_vol <- median(BSDE$Liquid, na.rm=T)
sd_BSDE_vol <- sd(BSDE$Liquid, na.rm=T)
max_BSDE_vol <- max(BSDE$Liquid, na.rm=T)
min_BSDE_vol <- min(BSDE$Liquid, na.rm=T)
#Tabel untuk Return BSDE
DStat_BSDE_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BSDE_dret,median_BSDE_dret,sd_BSDE_dret,max_BSDE_dret,min_BSDE_dret)
)
DStat_BSDE_dret#Tabel untuk Volume BSDE
DStat_BSDE_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BSDE_vol,median_BSDE_vol,sd_BSDE_vol,max_BSDE_vol,min_BSDE_vol)
)
DStat_BSDE_vol#Daily Return of BTON
avg_BTON_dret <- mean(BTON$dret, na.rm=T)
median_BTON_dret <- median(BTON$dret, na.rm=T)
sd_BTON_dret <- sd(BTON$dret, na.rm=T)
max_BTON_dret <- max(BTON$dret, na.rm=T)
min_BTON_dret <- min(BTON$dret, na.rm=T)
#Volume of BTON
avg_BTON_vol <- mean(BTON$Liquid, na.rm=T)
median_BTON_vol <- median(BTON$Liquid, na.rm=T)
sd_BTON_vol <- sd(BTON$Liquid, na.rm=T)
max_BTON_vol <- max(BTON$Liquid, na.rm=T)
min_BTON_vol <- min(BTON$Liquid, na.rm=T)
#Tabel untuk Return BTON
DStat_BTON_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BTON_dret,median_BTON_dret,sd_BTON_dret,max_BTON_dret,min_BTON_dret)
)
DStat_BTON_dret#Tabel untuk Volume BTON
DStat_BTON_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BTON_vol,median_BTON_vol,sd_BTON_vol,max_BTON_vol,min_BTON_vol)
)
DStat_BTON_vol#Daily Return of BULL
avg_BULL_dret <- mean(BULL$dret, na.rm=T)
median_BULL_dret <- median(BULL$dret, na.rm=T)
sd_BULL_dret <- sd(BULL$dret, na.rm=T)
max_BULL_dret <- max(BULL$dret, na.rm=T)
min_BULL_dret <- min(BULL$dret, na.rm=T)
#Volume of BULL
avg_BULL_vol <- mean(BULL$Liquid, na.rm=T)
median_BULL_vol <- median(BULL$Liquid, na.rm=T)
sd_BULL_vol <- sd(BULL$Liquid, na.rm=T)
max_BULL_vol <- max(BULL$Liquid, na.rm=T)
min_BULL_vol <- min(BULL$Liquid, na.rm=T)
#Tabel untuk Return BULL
DStat_BULL_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BULL_dret,median_BULL_dret,sd_BULL_dret,max_BULL_dret,min_BULL_dret)
)
DStat_BULL_dret#Tabel untuk Volume BULL
DStat_BULL_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BULL_vol,median_BULL_vol,sd_BULL_vol,max_BULL_vol,min_BULL_vol)
)
DStat_BULL_vol#Daily Return of BUMI
avg_BUMI_dret <- mean(BUMI$dret, na.rm=T)
median_BUMI_dret <- median(BUMI$dret, na.rm=T)
sd_BUMI_dret <- sd(BUMI$dret, na.rm=T)
max_BUMI_dret <- max(BUMI$dret, na.rm=T)
min_BUMI_dret <- min(BUMI$dret, na.rm=T)
#Volume of BUMI
avg_BUMI_vol <- mean(BUMI$Liquid, na.rm=T)
median_BUMI_vol <- median(BUMI$Liquid, na.rm=T)
sd_BUMI_vol <- sd(BUMI$Liquid, na.rm=T)
max_BUMI_vol <- max(BUMI$Liquid, na.rm=T)
min_BUMI_vol <- min(BUMI$Liquid, na.rm=T)
#Tabel untuk Return BUMI
DStat_BUMI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_BUMI_dret,median_BUMI_dret,sd_BUMI_dret,max_BUMI_dret,min_BUMI_dret)
)
DStat_BUMI_dret#Tabel untuk Volume BUMI
DStat_BUMI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_BUMI_vol,median_BUMI_vol,sd_BUMI_vol,max_BUMI_vol,min_BUMI_vol)
)
DStat_BUMI_vol#Daily Return of CASS
avg_CASS_dret <- mean(CASS$dret, na.rm=T)
median_CASS_dret <- median(CASS$dret, na.rm=T)
sd_CASS_dret <- sd(CASS$dret, na.rm=T)
max_CASS_dret <- max(CASS$dret, na.rm=T)
min_CASS_dret <- min(CASS$dret, na.rm=T)
#Volume of CASS
avg_CASS_vol <- mean(CASS$Liquid, na.rm=T)
median_CASS_vol <- median(CASS$Liquid, na.rm=T)
sd_CASS_vol <- sd(CASS$Liquid, na.rm=T)
max_CASS_vol <- max(CASS$Liquid, na.rm=T)
min_CASS_vol <- min(CASS$Liquid, na.rm=T)
#Tabel untuk Return CASS
DStat_CASS_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_CASS_dret,median_CASS_dret,sd_CASS_dret,max_CASS_dret,min_CASS_dret)
)
DStat_CASS_dret#Tabel untuk Volume CASS
DStat_CASS_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_CASS_vol,median_CASS_vol,sd_CASS_vol,max_CASS_vol,min_CASS_vol)
)
DStat_CASS_vol#Daily Return of CEKA
avg_CEKA_dret <- mean(CEKA$dret, na.rm=T)
median_CEKA_dret <- median(CEKA$dret, na.rm=T)
sd_CEKA_dret <- sd(CEKA$dret, na.rm=T)
max_CEKA_dret <- max(CEKA$dret, na.rm=T)
min_CEKA_dret <- min(CEKA$dret, na.rm=T)
#Volume of CEKA
avg_CEKA_vol <- mean(CEKA$Liquid, na.rm=T)
median_CEKA_vol <- median(CEKA$Liquid, na.rm=T)
sd_CEKA_vol <- sd(CEKA$Liquid, na.rm=T)
max_CEKA_vol <- max(CEKA$Liquid, na.rm=T)
min_CEKA_vol <- min(CEKA$Liquid, na.rm=T)
#Tabel untuk Return CEKA
DStat_CEKA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_CEKA_dret,median_CEKA_dret,sd_CEKA_dret,max_CEKA_dret,min_CEKA_dret)
)
DStat_CEKA_dret#Tabel untuk Volume CEKA
DStat_CEKA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_CEKA_vol,median_CEKA_vol,sd_CEKA_vol,max_CEKA_vol,min_CEKA_vol)
)
DStat_CEKA_vol#Daily Return of CENT
avg_CENT_dret <- mean(CENT$dret, na.rm=T)
median_CENT_dret <- median(CENT$dret, na.rm=T)
sd_CENT_dret <- sd(CENT$dret, na.rm=T)
max_CENT_dret <- max(CENT$dret, na.rm=T)
min_CENT_dret <- min(CENT$dret, na.rm=T)
#Volume of CENT
avg_CENT_vol <- mean(CENT$Liquid, na.rm=T)
median_CENT_vol <- median(CENT$Liquid, na.rm=T)
sd_CENT_vol <- sd(CENT$Liquid, na.rm=T)
max_CENT_vol <- max(CENT$Liquid, na.rm=T)
min_CENT_vol <- min(CENT$Liquid, na.rm=T)
#Tabel untuk Return CENT
DStat_CENT_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_CENT_dret,median_CENT_dret,sd_CENT_dret,max_CENT_dret,min_CENT_dret)
)
DStat_CENT_dret#Tabel untuk Volume CENT
DStat_CENT_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_CENT_vol,median_CENT_vol,sd_CENT_vol,max_CENT_vol,min_CENT_vol)
)
DStat_CENT_vol#Daily Return of CMNP
avg_CMNP_dret <- mean(CMNP$dret, na.rm=T)
median_CMNP_dret <- median(CMNP$dret, na.rm=T)
sd_CMNP_dret <- sd(CMNP$dret, na.rm=T)
max_CMNP_dret <- max(CMNP$dret, na.rm=T)
min_CMNP_dret <- min(CMNP$dret, na.rm=T)
#Volume of CMNP
avg_CMNP_vol <- mean(CMNP$Liquid, na.rm=T)
median_CMNP_vol <- median(CMNP$Liquid, na.rm=T)
sd_CMNP_vol <- sd(CMNP$Liquid, na.rm=T)
max_CMNP_vol <- max(CMNP$Liquid, na.rm=T)
min_CMNP_vol <- min(CMNP$Liquid, na.rm=T)
#Tabel untuk Return CMNP
DStat_CMNP_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_CMNP_dret,median_CMNP_dret,sd_CMNP_dret,max_CMNP_dret,min_CMNP_dret)
)
DStat_CMNP_dret#Tabel untuk Volume CMNP
DStat_CMNP_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_CMNP_vol,median_CMNP_vol,sd_CMNP_vol,max_CMNP_vol,min_CMNP_vol)
)
DStat_CMNP_vol#Daily Return of CPIN
avg_CPIN_dret <- mean(CPIN$dret, na.rm=T)
median_CPIN_dret <- median(CPIN$dret, na.rm=T)
sd_CPIN_dret <- sd(CPIN$dret, na.rm=T)
max_CPIN_dret <- max(CPIN$dret, na.rm=T)
min_CPIN_dret <- min(CPIN$dret, na.rm=T)
#Volume of CPIN
avg_CPIN_vol <- mean(CPIN$Liquid, na.rm=T)
median_CPIN_vol <- median(CPIN$Liquid, na.rm=T)
sd_CPIN_vol <- sd(CPIN$Liquid, na.rm=T)
max_CPIN_vol <- max(CPIN$Liquid, na.rm=T)
min_CPIN_vol <- min(CPIN$Liquid, na.rm=T)
#Tabel untuk Return CPIN
DStat_CPIN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_CPIN_dret,median_CPIN_dret,sd_CPIN_dret,max_CPIN_dret,min_CPIN_dret)
)
DStat_CPIN_dret#Tabel untuk Volume CPIN
DStat_CPIN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_CPIN_vol,median_CPIN_vol,sd_CPIN_vol,max_CPIN_vol,min_CPIN_vol)
)
DStat_CPIN_vol#Daily Return of CTRA
avg_CTRA_dret <- mean(CTRA$dret, na.rm=T)
median_CTRA_dret <- median(CTRA$dret, na.rm=T)
sd_CTRA_dret <- sd(CTRA$dret, na.rm=T)
max_CTRA_dret <- max(CTRA$dret, na.rm=T)
min_CTRA_dret <- min(CTRA$dret, na.rm=T)
#Volume of CTRA
avg_CTRA_vol <- mean(CTRA$Liquid, na.rm=T)
median_CTRA_vol <- median(CTRA$Liquid, na.rm=T)
sd_CTRA_vol <- sd(CTRA$Liquid, na.rm=T)
max_CTRA_vol <- max(CTRA$Liquid, na.rm=T)
min_CTRA_vol <- min(CTRA$Liquid, na.rm=T)
#Tabel untuk Return CTRA
DStat_CTRA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_CTRA_dret,median_CTRA_dret,sd_CTRA_dret,max_CTRA_dret,min_CTRA_dret)
)
DStat_CTRA_dret#Tabel untuk Volume CTRA
DStat_CTRA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_CTRA_vol,median_CTRA_vol,sd_CTRA_vol,max_CTRA_vol,min_CTRA_vol)
)
DStat_CTRA_vol#Daily Return of DART
avg_DART_dret <- mean(DART$dret, na.rm=T)
median_DART_dret <- median(DART$dret, na.rm=T)
sd_DART_dret <- sd(DART$dret, na.rm=T)
max_DART_dret <- max(DART$dret, na.rm=T)
min_DART_dret <- min(DART$dret, na.rm=T)
#Volume of DART
avg_DART_vol <- mean(DART$Liquid, na.rm=T)
median_DART_vol <- median(DART$Liquid, na.rm=T)
sd_DART_vol <- sd(DART$Liquid, na.rm=T)
max_DART_vol <- max(DART$Liquid, na.rm=T)
min_DART_vol <- min(DART$Liquid, na.rm=T)
#Tabel untuk Return DART
DStat_DART_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_DART_dret,median_DART_dret,sd_DART_dret,max_DART_dret,min_DART_dret)
)
DStat_DART_dret#Tabel untuk Volume DART
DStat_DART_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_DART_vol,median_DART_vol,sd_DART_vol,max_DART_vol,min_DART_vol)
)
DStat_DART_vol#Daily Return of DGIK
avg_DGIK_dret <- mean(DGIK$dret, na.rm=T)
median_DGIK_dret <- median(DGIK$dret, na.rm=T)
sd_DGIK_dret <- sd(DGIK$dret, na.rm=T)
max_DGIK_dret <- max(DGIK$dret, na.rm=T)
min_DGIK_dret <- min(DGIK$dret, na.rm=T)
#Volume of DGIK
avg_DGIK_vol <- mean(DGIK$Liquid, na.rm=T)
median_DGIK_vol <- median(DGIK$Liquid, na.rm=T)
sd_DGIK_vol <- sd(DGIK$Liquid, na.rm=T)
max_DGIK_vol <- max(DGIK$Liquid, na.rm=T)
min_DGIK_vol <- min(DGIK$Liquid, na.rm=T)
#Tabel untuk Return DGIK
DStat_DGIK_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_DGIK_dret,median_DGIK_dret,sd_DGIK_dret,max_DGIK_dret,min_DGIK_dret)
)
DStat_DGIK_dret#Tabel untuk Volume DGIK
DStat_DGIK_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_DGIK_vol,median_DGIK_vol,sd_DGIK_vol,max_DGIK_vol,min_DGIK_vol)
)
DStat_DGIK_vol#Daily Return of DOID
avg_DOID_dret <- mean(DOID$dret, na.rm=T)
median_DOID_dret <- median(DOID$dret, na.rm=T)
sd_DOID_dret <- sd(DOID$dret, na.rm=T)
max_DOID_dret <- max(DOID$dret, na.rm=T)
min_DOID_dret <- min(DOID$dret, na.rm=T)
#Volume of DOID
avg_DOID_vol <- mean(DOID$Liquid, na.rm=T)
median_DOID_vol <- median(DOID$Liquid, na.rm=T)
sd_DOID_vol <- sd(DOID$Liquid, na.rm=T)
max_DOID_vol <- max(DOID$Liquid, na.rm=T)
min_DOID_vol <- min(DOID$Liquid, na.rm=T)
#Tabel untuk Return DOID
DStat_DOID_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_DOID_dret,median_DOID_dret,sd_DOID_dret,max_DOID_dret,min_DOID_dret)
)
DStat_DOID_dret#Tabel untuk Volume DOID
DStat_DOID_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_DOID_vol,median_DOID_vol,sd_DOID_vol,max_DOID_vol,min_DOID_vol)
)
DStat_DOID_vol#Daily Return of DVLA
avg_DVLA_dret <- mean(DVLA$dret, na.rm=T)
median_DVLA_dret <- median(DVLA$dret, na.rm=T)
sd_DVLA_dret <- sd(DVLA$dret, na.rm=T)
max_DVLA_dret <- max(DVLA$dret, na.rm=T)
min_DVLA_dret <- min(DVLA$dret, na.rm=T)
#Volume of DVLA
avg_DVLA_vol <- mean(DVLA$Liquid, na.rm=T)
median_DVLA_vol <- median(DVLA$Liquid, na.rm=T)
sd_DVLA_vol <- sd(DVLA$Liquid, na.rm=T)
max_DVLA_vol <- max(DVLA$Liquid, na.rm=T)
min_DVLA_vol <- min(DVLA$Liquid, na.rm=T)
#Tabel untuk Return DVLA
DStat_DVLA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_DVLA_dret,median_DVLA_dret,sd_DVLA_dret,max_DVLA_dret,min_DVLA_dret)
)
DStat_DVLA_dret#Tabel untuk Volume DVLA
DStat_DVLA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_DVLA_vol,median_DVLA_vol,sd_DVLA_vol,max_DVLA_vol,min_DVLA_vol)
)
DStat_DVLA_vol#Daily Return of ELSA
avg_ELSA_dret <- mean(ELSA$dret, na.rm=T)
median_ELSA_dret <- median(ELSA$dret, na.rm=T)
sd_ELSA_dret <- sd(ELSA$dret, na.rm=T)
max_ELSA_dret <- max(ELSA$dret, na.rm=T)
min_ELSA_dret <- min(ELSA$dret, na.rm=T)
#Volume of ELSA
avg_ELSA_vol <- mean(ELSA$Liquid, na.rm=T)
median_ELSA_vol <- median(ELSA$Liquid, na.rm=T)
sd_ELSA_vol <- sd(ELSA$Liquid, na.rm=T)
max_ELSA_vol <- max(ELSA$Liquid, na.rm=T)
min_ELSA_vol <- min(ELSA$Liquid, na.rm=T)
#Tabel untuk Return ELSA
DStat_ELSA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ELSA_dret,median_ELSA_dret,sd_ELSA_dret,max_ELSA_dret,min_ELSA_dret)
)
DStat_ELSA_dret#Tabel untuk Volume ELSA
DStat_ELSA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ELSA_vol,median_ELSA_vol,sd_ELSA_vol,max_ELSA_vol,min_ELSA_vol)
)
DStat_ELSA_vol#Daily Return of EMTK
avg_EMTK_dret <- mean(EMTK$dret, na.rm=T)
median_EMTK_dret <- median(EMTK$dret, na.rm=T)
sd_EMTK_dret <- sd(EMTK$dret, na.rm=T)
max_EMTK_dret <- max(EMTK$dret, na.rm=T)
min_EMTK_dret <- min(EMTK$dret, na.rm=T)
#Volume of EMTK
avg_EMTK_vol <- mean(EMTK$Liquid, na.rm=T)
median_EMTK_vol <- median(EMTK$Liquid, na.rm=T)
sd_EMTK_vol <- sd(EMTK$Liquid, na.rm=T)
max_EMTK_vol <- max(EMTK$Liquid, na.rm=T)
min_EMTK_vol <- min(EMTK$Liquid, na.rm=T)
#Tabel untuk Return EMTK
DStat_EMTK_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_EMTK_dret,median_EMTK_dret,sd_EMTK_dret,max_EMTK_dret,min_EMTK_dret)
)
DStat_EMTK_dret#Tabel untuk Volume EMTK
DStat_EMTK_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_EMTK_vol,median_EMTK_vol,sd_EMTK_vol,max_EMTK_vol,min_EMTK_vol)
)
DStat_EMTK_vol#Daily Return of ERAA
avg_ERAA_dret <- mean(ERAA$dret, na.rm=T)
median_ERAA_dret <- median(ERAA$dret, na.rm=T)
sd_ERAA_dret <- sd(ERAA$dret, na.rm=T)
max_ERAA_dret <- max(ERAA$dret, na.rm=T)
min_ERAA_dret <- min(ERAA$dret, na.rm=T)
#Volume of ERAA
avg_ERAA_vol <- mean(ERAA$Liquid, na.rm=T)
median_ERAA_vol <- median(ERAA$Liquid, na.rm=T)
sd_ERAA_vol <- sd(ERAA$Liquid, na.rm=T)
max_ERAA_vol <- max(ERAA$Liquid, na.rm=T)
min_ERAA_vol <- min(ERAA$Liquid, na.rm=T)
#Tabel untuk Return ERAA
DStat_ERAA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ERAA_dret,median_ERAA_dret,sd_ERAA_dret,max_ERAA_dret,min_ERAA_dret)
)
DStat_ERAA_dret#Tabel untuk Volume ERAA
DStat_ERAA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ERAA_vol,median_ERAA_vol,sd_ERAA_vol,max_ERAA_vol,min_ERAA_vol)
)
DStat_ERAA_vol#Daily Return of EXCL
avg_EXCL_dret <- mean(EXCL$dret, na.rm=T)
median_EXCL_dret <- median(EXCL$dret, na.rm=T)
sd_EXCL_dret <- sd(EXCL$dret, na.rm=T)
max_EXCL_dret <- max(EXCL$dret, na.rm=T)
min_EXCL_dret <- min(EXCL$dret, na.rm=T)
#Volume of EXCL
avg_EXCL_vol <- mean(EXCL$Liquid, na.rm=T)
median_EXCL_vol <- median(EXCL$Liquid, na.rm=T)
sd_EXCL_vol <- sd(EXCL$Liquid, na.rm=T)
max_EXCL_vol <- max(EXCL$Liquid, na.rm=T)
min_EXCL_vol <- min(EXCL$Liquid, na.rm=T)
#Tabel untuk Return EXCL
DStat_EXCL_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_EXCL_dret,median_EXCL_dret,sd_EXCL_dret,max_EXCL_dret,min_EXCL_dret)
)
DStat_EXCL_dret#Tabel untuk Volume EXCL
DStat_EXCL_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_EXCL_vol,median_EXCL_vol,sd_EXCL_vol,max_EXCL_vol,min_EXCL_vol)
)
DStat_EXCL_vol#Daily Return of FPNI
avg_FPNI_dret <- mean(FPNI$dret, na.rm=T)
median_FPNI_dret <- median(FPNI$dret, na.rm=T)
sd_FPNI_dret <- sd(FPNI$dret, na.rm=T)
max_FPNI_dret <- max(FPNI$dret, na.rm=T)
min_FPNI_dret <- min(FPNI$dret, na.rm=T)
#Volume of FPNI
avg_FPNI_vol <- mean(FPNI$Liquid, na.rm=T)
median_FPNI_vol <- median(FPNI$Liquid, na.rm=T)
sd_FPNI_vol <- sd(FPNI$Liquid, na.rm=T)
max_FPNI_vol <- max(FPNI$Liquid, na.rm=T)
min_FPNI_vol <- min(FPNI$Liquid, na.rm=T)
#Tabel untuk Return FPNI
DStat_FPNI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_FPNI_dret,median_FPNI_dret,sd_FPNI_dret,max_FPNI_dret,min_FPNI_dret)
)
DStat_FPNI_dret#Tabel untuk Volume FPNI
DStat_FPNI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_FPNI_vol,median_FPNI_vol,sd_FPNI_vol,max_FPNI_vol,min_FPNI_vol)
)
DStat_FPNI_vol#Daily Return of FREN
avg_FREN_dret <- mean(FREN$dret, na.rm=T)
median_FREN_dret <- median(FREN$dret, na.rm=T)
sd_FREN_dret <- sd(FREN$dret, na.rm=T)
max_FREN_dret <- max(FREN$dret, na.rm=T)
min_FREN_dret <- min(FREN$dret, na.rm=T)
#Volume of FREN
avg_FREN_vol <- mean(FREN$Liquid, na.rm=T)
median_FREN_vol <- median(FREN$Liquid, na.rm=T)
sd_FREN_vol <- sd(FREN$Liquid, na.rm=T)
max_FREN_vol <- max(FREN$Liquid, na.rm=T)
min_FREN_vol <- min(FREN$Liquid, na.rm=T)
#Tabel untuk Return FREN
DStat_FREN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_FREN_dret,median_FREN_dret,sd_FREN_dret,max_FREN_dret,min_FREN_dret)
)
DStat_FREN_dret#Tabel untuk Volume FREN
DStat_FREN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_FREN_vol,median_FREN_vol,sd_FREN_vol,max_FREN_vol,min_FREN_vol)
)
DStat_FREN_vol#Daily Return of HRUM
avg_HRUM_dret <- mean(HRUM$dret, na.rm=T)
median_HRUM_dret <- median(HRUM$dret, na.rm=T)
sd_HRUM_dret <- sd(HRUM$dret, na.rm=T)
max_HRUM_dret <- max(HRUM$dret, na.rm=T)
min_HRUM_dret <- min(HRUM$dret, na.rm=T)
#Volume of HRUM
avg_HRUM_vol <- mean(HRUM$Liquid, na.rm=T)
median_HRUM_vol <- median(HRUM$Liquid, na.rm=T)
sd_HRUM_vol <- sd(HRUM$Liquid, na.rm=T)
max_HRUM_vol <- max(HRUM$Liquid, na.rm=T)
min_HRUM_vol <- min(HRUM$Liquid, na.rm=T)
#Tabel untuk Return HRUM
DStat_HRUM_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_HRUM_dret,median_HRUM_dret,sd_HRUM_dret,max_HRUM_dret,min_HRUM_dret)
)
DStat_HRUM_dret#Tabel untuk Volume HRUM
DStat_HRUM_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_HRUM_vol,median_HRUM_vol,sd_HRUM_vol,max_HRUM_vol,min_HRUM_vol)
)
DStat_HRUM_vol#Daily Return of ICBP
avg_ICBP_dret <- mean(ICBP$dret, na.rm=T)
median_ICBP_dret <- median(ICBP$dret, na.rm=T)
sd_ICBP_dret <- sd(ICBP$dret, na.rm=T)
max_ICBP_dret <- max(ICBP$dret, na.rm=T)
min_ICBP_dret <- min(ICBP$dret, na.rm=T)
#Volume of ICBP
avg_ICBP_vol <- mean(ICBP$Liquid, na.rm=T)
median_ICBP_vol <- median(ICBP$Liquid, na.rm=T)
sd_ICBP_vol <- sd(ICBP$Liquid, na.rm=T)
max_ICBP_vol <- max(ICBP$Liquid, na.rm=T)
min_ICBP_vol <- min(ICBP$Liquid, na.rm=T)
#Tabel untuk Return ICBP
DStat_ICBP_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ICBP_dret,median_ICBP_dret,sd_ICBP_dret,max_ICBP_dret,min_ICBP_dret)
)
DStat_ICBP_dret#Tabel untuk Volume ICBP
DStat_ICBP_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ICBP_vol,median_ICBP_vol,sd_ICBP_vol,max_ICBP_vol,min_ICBP_vol)
)
DStat_ICBP_vol#Daily Return of INAF
avg_INAF_dret <- mean(INAF$dret, na.rm=T)
median_INAF_dret <- median(INAF$dret, na.rm=T)
sd_INAF_dret <- sd(INAF$dret, na.rm=T)
max_INAF_dret <- max(INAF$dret, na.rm=T)
min_INAF_dret <- min(INAF$dret, na.rm=T)
#Volume of INAF
avg_INAF_vol <- mean(INAF$Liquid, na.rm=T)
median_INAF_vol <- median(INAF$Liquid, na.rm=T)
sd_INAF_vol <- sd(INAF$Liquid, na.rm=T)
max_INAF_vol <- max(INAF$Liquid, na.rm=T)
min_INAF_vol <- min(INAF$Liquid, na.rm=T)
#Tabel untuk Return INAF
DStat_INAF_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_INAF_dret,median_INAF_dret,sd_INAF_dret,max_INAF_dret,min_INAF_dret)
)
DStat_INAF_dret#Tabel untuk Volume INAF
DStat_INAF_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_INAF_vol,median_INAF_vol,sd_INAF_vol,max_INAF_vol,min_INAF_vol)
)
DStat_INAF_vol#Daily Return of INCO
avg_INCO_dret <- mean(INCO$dret, na.rm=T)
median_INCO_dret <- median(INCO$dret, na.rm=T)
sd_INCO_dret <- sd(INCO$dret, na.rm=T)
max_INCO_dret <- max(INCO$dret, na.rm=T)
min_INCO_dret <- min(INCO$dret, na.rm=T)
#Volume of INCO
avg_INCO_vol <- mean(INCO$Liquid, na.rm=T)
median_INCO_vol <- median(INCO$Liquid, na.rm=T)
sd_INCO_vol <- sd(INCO$Liquid, na.rm=T)
max_INCO_vol <- max(INCO$Liquid, na.rm=T)
min_INCO_vol <- min(INCO$Liquid, na.rm=T)
#Tabel untuk Return INCO
DStat_INCO_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_INCO_dret,median_INCO_dret,sd_INCO_dret,max_INCO_dret,min_INCO_dret)
)
DStat_INCO_dret#Tabel untuk Volume INCO
DStat_INCO_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_INCO_vol,median_INCO_vol,sd_INCO_vol,max_INCO_vol,min_INCO_vol)
)
DStat_INCO_vol#Daily Return of INDR
avg_INDR_dret <- mean(INDR$dret, na.rm=T)
median_INDR_dret <- median(INDR$dret, na.rm=T)
sd_INDR_dret <- sd(INDR$dret, na.rm=T)
max_INDR_dret <- max(INDR$dret, na.rm=T)
min_INDR_dret <- min(INDR$dret, na.rm=T)
#Volume of INDR
avg_INDR_vol <- mean(INDR$Liquid, na.rm=T)
median_INDR_vol <- median(INDR$Liquid, na.rm=T)
sd_INDR_vol <- sd(INDR$Liquid, na.rm=T)
max_INDR_vol <- max(INDR$Liquid, na.rm=T)
min_INDR_vol <- min(INDR$Liquid, na.rm=T)
#Tabel untuk Return INDR
DStat_INDR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_INDR_dret,median_INDR_dret,sd_INDR_dret,max_INDR_dret,min_INDR_dret)
)
DStat_INDR_dret#Tabel untuk Volume INDR
DStat_INDR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_INDR_vol,median_INDR_vol,sd_INDR_vol,max_INDR_vol,min_INDR_vol)
)
DStat_INDR_vol#Daily Return of INDY
avg_INDY_dret <- mean(INDY$dret, na.rm=T)
median_INDY_dret <- median(INDY$dret, na.rm=T)
sd_INDY_dret <- sd(INDY$dret, na.rm=T)
max_INDY_dret <- max(INDY$dret, na.rm=T)
min_INDY_dret <- min(INDY$dret, na.rm=T)
#Volume of INDY
avg_INDY_vol <- mean(INDY$Liquid, na.rm=T)
median_INDY_vol <- median(INDY$Liquid, na.rm=T)
sd_INDY_vol <- sd(INDY$Liquid, na.rm=T)
max_INDY_vol <- max(INDY$Liquid, na.rm=T)
min_INDY_vol <- min(INDY$Liquid, na.rm=T)
#Tabel untuk Return INDY
DStat_INDY_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_INDY_dret,median_INDY_dret,sd_INDY_dret,max_INDY_dret,min_INDY_dret)
)
DStat_INDY_dret#Tabel untuk Volume INDY
DStat_INDY_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_INDY_vol,median_INDY_vol,sd_INDY_vol,max_INDY_vol,min_INDY_vol)
)
DStat_INDY_vol#Daily Return of INKP
avg_INKP_dret <- mean(INKP$dret, na.rm=T)
median_INKP_dret <- median(INKP$dret, na.rm=T)
sd_INKP_dret <- sd(INKP$dret, na.rm=T)
max_INKP_dret <- max(INKP$dret, na.rm=T)
min_INKP_dret <- min(INKP$dret, na.rm=T)
#Volume of INKP
avg_INKP_vol <- mean(INKP$Liquid, na.rm=T)
median_INKP_vol <- median(INKP$Liquid, na.rm=T)
sd_INKP_vol <- sd(INKP$Liquid, na.rm=T)
max_INKP_vol <- max(INKP$Liquid, na.rm=T)
min_INKP_vol <- min(INKP$Liquid, na.rm=T)
#Tabel untuk Return INKP
DStat_INKP_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_INKP_dret,median_INKP_dret,sd_INKP_dret,max_INKP_dret,min_INKP_dret)
)
DStat_INKP_dret#Tabel untuk Volume INKP
DStat_INKP_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_INKP_vol,median_INKP_vol,sd_INKP_vol,max_INKP_vol,min_INKP_vol)
)
DStat_INKP_vol#Daily Return of INTA
avg_INTA_dret <- mean(INTA$dret, na.rm=T)
median_INTA_dret <- median(INTA$dret, na.rm=T)
sd_INTA_dret <- sd(INTA$dret, na.rm=T)
max_INTA_dret <- max(INTA$dret, na.rm=T)
min_INTA_dret <- min(INTA$dret, na.rm=T)
#Volume of INTA
avg_INTA_vol <- mean(INTA$Liquid, na.rm=T)
median_INTA_vol <- median(INTA$Liquid, na.rm=T)
sd_INTA_vol <- sd(INTA$Liquid, na.rm=T)
max_INTA_vol <- max(INTA$Liquid, na.rm=T)
min_INTA_vol <- min(INTA$Liquid, na.rm=T)
#Tabel untuk Return INTA
DStat_INTA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_INTA_dret,median_INTA_dret,sd_INTA_dret,max_INTA_dret,min_INTA_dret)
)
DStat_INTA_dret#Tabel untuk Volume INTA
DStat_INTA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_INTA_vol,median_INTA_vol,sd_INTA_vol,max_INTA_vol,min_INTA_vol)
)
DStat_INTA_vol#Daily Return of JECC
avg_JECC_dret <- mean(JECC$dret, na.rm=T)
median_JECC_dret <- median(JECC$dret, na.rm=T)
sd_JECC_dret <- sd(JECC$dret, na.rm=T)
max_JECC_dret <- max(JECC$dret, na.rm=T)
min_JECC_dret <- min(JECC$dret, na.rm=T)
#Volume of JECC
avg_JECC_vol <- mean(JECC$Liquid, na.rm=T)
median_JECC_vol <- median(JECC$Liquid, na.rm=T)
sd_JECC_vol <- sd(JECC$Liquid, na.rm=T)
max_JECC_vol <- max(JECC$Liquid, na.rm=T)
min_JECC_vol <- min(JECC$Liquid, na.rm=T)
#Tabel untuk Return JECC
DStat_JECC_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_JECC_dret,median_JECC_dret,sd_JECC_dret,max_JECC_dret,min_JECC_dret)
)
DStat_JECC_dret#Tabel untuk Volume JECC
DStat_JECC_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_JECC_vol,median_JECC_vol,sd_JECC_vol,max_JECC_vol,min_JECC_vol)
)
DStat_JECC_vol#Daily Return of JRPT
avg_JRPT_dret <- mean(JRPT$dret, na.rm=T)
median_JRPT_dret <- median(JRPT$dret, na.rm=T)
sd_JRPT_dret <- sd(JRPT$dret, na.rm=T)
max_JRPT_dret <- max(JRPT$dret, na.rm=T)
min_JRPT_dret <- min(JRPT$dret, na.rm=T)
#Volume of JRPT
avg_JRPT_vol <- mean(JRPT$Liquid, na.rm=T)
median_JRPT_vol <- median(JRPT$Liquid, na.rm=T)
sd_JRPT_vol <- sd(JRPT$Liquid, na.rm=T)
max_JRPT_vol <- max(JRPT$Liquid, na.rm=T)
min_JRPT_vol <- min(JRPT$Liquid, na.rm=T)
#Tabel untuk Return JRPT
DStat_JRPT_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_JRPT_dret,median_JRPT_dret,sd_JRPT_dret,max_JRPT_dret,min_JRPT_dret)
)
DStat_JRPT_dret#Tabel untuk Volume JRPT
DStat_JRPT_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_JRPT_vol,median_JRPT_vol,sd_JRPT_vol,max_JRPT_vol,min_JRPT_vol)
)
DStat_JRPT_vol#Daily Return of JSMR
avg_JSMR_dret <- mean(JSMR$dret, na.rm=T)
median_JSMR_dret <- median(JSMR$dret, na.rm=T)
sd_JSMR_dret <- sd(JSMR$dret, na.rm=T)
max_JSMR_dret <- max(JSMR$dret, na.rm=T)
min_JSMR_dret <- min(JSMR$dret, na.rm=T)
#Volume of JSMR
avg_JSMR_vol <- mean(JSMR$Liquid, na.rm=T)
median_JSMR_vol <- median(JSMR$Liquid, na.rm=T)
sd_JSMR_vol <- sd(JSMR$Liquid, na.rm=T)
max_JSMR_vol <- max(JSMR$Liquid, na.rm=T)
min_JSMR_vol <- min(JSMR$Liquid, na.rm=T)
#Tabel untuk Return JSMR
DStat_JSMR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_JSMR_dret,median_JSMR_dret,sd_JSMR_dret,max_JSMR_dret,min_JSMR_dret)
)
DStat_JSMR_dret#Tabel untuk Volume JSMR
DStat_JSMR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_JSMR_vol,median_JSMR_vol,sd_JSMR_vol,max_JSMR_vol,min_JSMR_vol)
)
DStat_JSMR_vol#Daily Return of KAEF
avg_KAEF_dret <- mean(KAEF$dret, na.rm=T)
median_KAEF_dret <- median(KAEF$dret, na.rm=T)
sd_KAEF_dret <- sd(KAEF$dret, na.rm=T)
max_KAEF_dret <- max(KAEF$dret, na.rm=T)
min_KAEF_dret <- min(KAEF$dret, na.rm=T)
#Volume of KAEF
avg_KAEF_vol <- mean(KAEF$Liquid, na.rm=T)
median_KAEF_vol <- median(KAEF$Liquid, na.rm=T)
sd_KAEF_vol <- sd(KAEF$Liquid, na.rm=T)
max_KAEF_vol <- max(KAEF$Liquid, na.rm=T)
min_KAEF_vol <- min(KAEF$Liquid, na.rm=T)
#Tabel untuk Return KAEF
DStat_KAEF_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_KAEF_dret,median_KAEF_dret,sd_KAEF_dret,max_KAEF_dret,min_KAEF_dret)
)
DStat_KAEF_dret#Tabel untuk Volume KAEF
DStat_KAEF_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_KAEF_vol,median_KAEF_vol,sd_KAEF_vol,max_KAEF_vol,min_KAEF_vol)
)
DStat_KAEF_vol#Daily Return of KBLI
avg_KBLI_dret <- mean(KBLI$dret, na.rm=T)
median_KBLI_dret <- median(KBLI$dret, na.rm=T)
sd_KBLI_dret <- sd(KBLI$dret, na.rm=T)
max_KBLI_dret <- max(KBLI$dret, na.rm=T)
min_KBLI_dret <- min(KBLI$dret, na.rm=T)
#Volume of KBLI
avg_KBLI_vol <- mean(KBLI$Liquid, na.rm=T)
median_KBLI_vol <- median(KBLI$Liquid, na.rm=T)
sd_KBLI_vol <- sd(KBLI$Liquid, na.rm=T)
max_KBLI_vol <- max(KBLI$Liquid, na.rm=T)
min_KBLI_vol <- min(KBLI$Liquid, na.rm=T)
#Tabel untuk Return KBLI
DStat_KBLI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_KBLI_dret,median_KBLI_dret,sd_KBLI_dret,max_KBLI_dret,min_KBLI_dret)
)
DStat_KBLI_dret#Tabel untuk Volume KBLI
DStat_KBLI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_KBLI_vol,median_KBLI_vol,sd_KBLI_vol,max_KBLI_vol,min_KBLI_vol)
)
DStat_KBLI_vol#Daily Return of KLBF
avg_KLBF_dret <- mean(KLBF$dret, na.rm=T)
median_KLBF_dret <- median(KLBF$dret, na.rm=T)
sd_KLBF_dret <- sd(KLBF$dret, na.rm=T)
max_KLBF_dret <- max(KLBF$dret, na.rm=T)
min_KLBF_dret <- min(KLBF$dret, na.rm=T)
#Volume of KLBF
avg_KLBF_vol <- mean(KLBF$Liquid, na.rm=T)
median_KLBF_vol <- median(KLBF$Liquid, na.rm=T)
sd_KLBF_vol <- sd(KLBF$Liquid, na.rm=T)
max_KLBF_vol <- max(KLBF$Liquid, na.rm=T)
min_KLBF_vol <- min(KLBF$Liquid, na.rm=T)
#Tabel untuk Return KLBF
DStat_KLBF_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_KLBF_dret,median_KLBF_dret,sd_KLBF_dret,max_KLBF_dret,min_KLBF_dret)
)
DStat_KLBF_dret#Tabel untuk Volume KLBF
DStat_KLBF_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_KLBF_vol,median_KLBF_vol,sd_KLBF_vol,max_KLBF_vol,min_KLBF_vol)
)
DStat_KLBF_vol#Daily Return of KOIN
avg_KOIN_dret <- mean(KOIN$dret, na.rm=T)
median_KOIN_dret <- median(KOIN$dret, na.rm=T)
sd_KOIN_dret <- sd(KOIN$dret, na.rm=T)
max_KOIN_dret <- max(KOIN$dret, na.rm=T)
min_KOIN_dret <- min(KOIN$dret, na.rm=T)
#Volume of KOIN
avg_KOIN_vol <- mean(KOIN$Liquid, na.rm=T)
median_KOIN_vol <- median(KOIN$Liquid, na.rm=T)
sd_KOIN_vol <- sd(KOIN$Liquid, na.rm=T)
max_KOIN_vol <- max(KOIN$Liquid, na.rm=T)
min_KOIN_vol <- min(KOIN$Liquid, na.rm=T)
#Tabel untuk Return KOIN
DStat_KOIN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_KOIN_dret,median_KOIN_dret,sd_KOIN_dret,max_KOIN_dret,min_KOIN_dret)
)
DStat_KOIN_dret#Tabel untuk Volume KOIN
DStat_KOIN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_KOIN_vol,median_KOIN_vol,sd_KOIN_vol,max_KOIN_vol,min_KOIN_vol)
)
DStat_KOIN_vol#Daily Return of KPIG
avg_KPIG_dret <- mean(KPIG$dret, na.rm=T)
median_KPIG_dret <- median(KPIG$dret, na.rm=T)
sd_KPIG_dret <- sd(KPIG$dret, na.rm=T)
max_KPIG_dret <- max(KPIG$dret, na.rm=T)
min_KPIG_dret <- min(KPIG$dret, na.rm=T)
#Volume of KPIG
avg_KPIG_vol <- mean(KPIG$Liquid, na.rm=T)
median_KPIG_vol <- median(KPIG$Liquid, na.rm=T)
sd_KPIG_vol <- sd(KPIG$Liquid, na.rm=T)
max_KPIG_vol <- max(KPIG$Liquid, na.rm=T)
min_KPIG_vol <- min(KPIG$Liquid, na.rm=T)
#Tabel untuk Return KPIG
DStat_KPIG_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_KPIG_dret,median_KPIG_dret,sd_KPIG_dret,max_KPIG_dret,min_KPIG_dret)
)
DStat_KPIG_dret#Tabel untuk Volume KPIG
DStat_KPIG_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_KPIG_vol,median_KPIG_vol,sd_KPIG_vol,max_KPIG_vol,min_KPIG_vol)
)
DStat_KPIG_vol#Daily Return of LMPI
avg_LMPI_dret <- mean(LMPI$dret, na.rm=T)
median_LMPI_dret <- median(LMPI$dret, na.rm=T)
sd_LMPI_dret <- sd(LMPI$dret, na.rm=T)
max_LMPI_dret <- max(LMPI$dret, na.rm=T)
min_LMPI_dret <- min(LMPI$dret, na.rm=T)
#Volume of LMPI
avg_LMPI_vol <- mean(LMPI$Liquid, na.rm=T)
median_LMPI_vol <- median(LMPI$Liquid, na.rm=T)
sd_LMPI_vol <- sd(LMPI$Liquid, na.rm=T)
max_LMPI_vol <- max(LMPI$Liquid, na.rm=T)
min_LMPI_vol <- min(LMPI$Liquid, na.rm=T)
#Tabel untuk Return LMPI
DStat_LMPI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_LMPI_dret,median_LMPI_dret,sd_LMPI_dret,max_LMPI_dret,min_LMPI_dret)
)
DStat_LMPI_dret#Tabel untuk Volume LMPI
DStat_LMPI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_LMPI_vol,median_LMPI_vol,sd_LMPI_vol,max_LMPI_vol,min_LMPI_vol)
)
DStat_LMPI_vol#Daily Return of MEDC
avg_MEDC_dret <- mean(MEDC$dret, na.rm=T)
median_MEDC_dret <- median(MEDC$dret, na.rm=T)
sd_MEDC_dret <- sd(MEDC$dret, na.rm=T)
max_MEDC_dret <- max(MEDC$dret, na.rm=T)
min_MEDC_dret <- min(MEDC$dret, na.rm=T)
#Volume of MEDC
avg_MEDC_vol <- mean(MEDC$Liquid, na.rm=T)
median_MEDC_vol <- median(MEDC$Liquid, na.rm=T)
sd_MEDC_vol <- sd(MEDC$Liquid, na.rm=T)
max_MEDC_vol <- max(MEDC$Liquid, na.rm=T)
min_MEDC_vol <- min(MEDC$Liquid, na.rm=T)
#Tabel untuk Return MEDC
DStat_MEDC_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_MEDC_dret,median_MEDC_dret,sd_MEDC_dret,max_MEDC_dret,min_MEDC_dret)
)
DStat_MEDC_dret#Tabel untuk Volume MEDC
DStat_MEDC_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_MEDC_vol,median_MEDC_vol,sd_MEDC_vol,max_MEDC_vol,min_MEDC_vol)
)
DStat_MEDC_vol#Daily Return of MERK
avg_MERK_dret <- mean(MERK$dret, na.rm=T)
median_MERK_dret <- median(MERK$dret, na.rm=T)
sd_MERK_dret <- sd(MERK$dret, na.rm=T)
max_MERK_dret <- max(MERK$dret, na.rm=T)
min_MERK_dret <- min(MERK$dret, na.rm=T)
#Volume of MERK
avg_MERK_vol <- mean(MERK$Liquid, na.rm=T)
median_MERK_vol <- median(MERK$Liquid, na.rm=T)
sd_MERK_vol <- sd(MERK$Liquid, na.rm=T)
max_MERK_vol <- max(MERK$Liquid, na.rm=T)
min_MERK_vol <- min(MERK$Liquid, na.rm=T)
#Tabel untuk Return MERK
DStat_MERK_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_MERK_dret,median_MERK_dret,sd_MERK_dret,max_MERK_dret,min_MERK_dret)
)
DStat_MERK_dret#Tabel untuk Volume MERK
DStat_MERK_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_MERK_vol,median_MERK_vol,sd_MERK_vol,max_MERK_vol,min_MERK_vol)
)
DStat_MERK_vol#Daily Return of META
avg_META_dret <- mean(META$dret, na.rm=T)
median_META_dret <- median(META$dret, na.rm=T)
sd_META_dret <- sd(META$dret, na.rm=T)
max_META_dret <- max(META$dret, na.rm=T)
min_META_dret <- min(META$dret, na.rm=T)
#Volume of META
avg_META_vol <- mean(META$Liquid, na.rm=T)
median_META_vol <- median(META$Liquid, na.rm=T)
sd_META_vol <- sd(META$Liquid, na.rm=T)
max_META_vol <- max(META$Liquid, na.rm=T)
min_META_vol <- min(META$Liquid, na.rm=T)
#Tabel untuk Return META
DStat_META_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_META_dret,median_META_dret,sd_META_dret,max_META_dret,min_META_dret)
)
DStat_META_dret#Tabel untuk Volume META
DStat_META_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_META_vol,median_META_vol,sd_META_vol,max_META_vol,min_META_vol)
)
DStat_META_vol#Daily Return of MLBI
avg_MLBI_dret <- mean(MLBI$dret, na.rm=T)
median_MLBI_dret <- median(MLBI$dret, na.rm=T)
sd_MLBI_dret <- sd(MLBI$dret, na.rm=T)
max_MLBI_dret <- max(MLBI$dret, na.rm=T)
min_MLBI_dret <- min(MLBI$dret, na.rm=T)
#Volume of MLBI
avg_MLBI_vol <- mean(MLBI$Liquid, na.rm=T)
median_MLBI_vol <- median(MLBI$Liquid, na.rm=T)
sd_MLBI_vol <- sd(MLBI$Liquid, na.rm=T)
max_MLBI_vol <- max(MLBI$Liquid, na.rm=T)
min_MLBI_vol <- min(MLBI$Liquid, na.rm=T)
#Tabel untuk Return MLBI
DStat_MLBI_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_MLBI_dret,median_MLBI_dret,sd_MLBI_dret,max_MLBI_dret,min_MLBI_dret)
)
DStat_MLBI_dret#Tabel untuk Volume MLBI
DStat_MLBI_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_MLBI_vol,median_MLBI_vol,sd_MLBI_vol,max_MLBI_vol,min_MLBI_vol)
)
DStat_MLBI_vol#Daily Return of MLIA
avg_MLIA_dret <- mean(MLIA$dret, na.rm=T)
median_MLIA_dret <- median(MLIA$dret, na.rm=T)
sd_MLIA_dret <- sd(MLIA$dret, na.rm=T)
max_MLIA_dret <- max(MLIA$dret, na.rm=T)
min_MLIA_dret <- min(MLIA$dret, na.rm=T)
#Volume of MLIA
avg_MLIA_vol <- mean(MLIA$Liquid, na.rm=T)
median_MLIA_vol <- median(MLIA$Liquid, na.rm=T)
sd_MLIA_vol <- sd(MLIA$Liquid, na.rm=T)
max_MLIA_vol <- max(MLIA$Liquid, na.rm=T)
min_MLIA_vol <- min(MLIA$Liquid, na.rm=T)
#Tabel untuk Return MLIA
DStat_MLIA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_MLIA_dret,median_MLIA_dret,sd_MLIA_dret,max_MLIA_dret,min_MLIA_dret)
)
DStat_MLIA_dret#Tabel untuk Volume MLIA
DStat_MLIA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_MLIA_vol,median_MLIA_vol,sd_MLIA_vol,max_MLIA_vol,min_MLIA_vol)
)
DStat_MLIA_vol#Daily Return of MTDL
avg_MTDL_dret <- mean(MTDL$dret, na.rm=T)
median_MTDL_dret <- median(MTDL$dret, na.rm=T)
sd_MTDL_dret <- sd(MTDL$dret, na.rm=T)
max_MTDL_dret <- max(MTDL$dret, na.rm=T)
min_MTDL_dret <- min(MTDL$dret, na.rm=T)
#Volume of MTDL
avg_MTDL_vol <- mean(MTDL$Liquid, na.rm=T)
median_MTDL_vol <- median(MTDL$Liquid, na.rm=T)
sd_MTDL_vol <- sd(MTDL$Liquid, na.rm=T)
max_MTDL_vol <- max(MTDL$Liquid, na.rm=T)
min_MTDL_vol <- min(MTDL$Liquid, na.rm=T)
#Tabel untuk Return MTDL
DStat_MTDL_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_MTDL_dret,median_MTDL_dret,sd_MTDL_dret,max_MTDL_dret,min_MTDL_dret)
)
DStat_MTDL_dret#Tabel untuk Volume MTDL
DStat_MTDL_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_MTDL_vol,median_MTDL_vol,sd_MTDL_vol,max_MTDL_vol,min_MTDL_vol)
)
DStat_MTDL_vol#Daily Return of MYOH
avg_MYOH_dret <- mean(MYOH$dret, na.rm=T)
median_MYOH_dret <- median(MYOH$dret, na.rm=T)
sd_MYOH_dret <- sd(MYOH$dret, na.rm=T)
max_MYOH_dret <- max(MYOH$dret, na.rm=T)
min_MYOH_dret <- min(MYOH$dret, na.rm=T)
#Volume of MYOH
avg_MYOH_vol <- mean(MYOH$Liquid, na.rm=T)
median_MYOH_vol <- median(MYOH$Liquid, na.rm=T)
sd_MYOH_vol <- sd(MYOH$Liquid, na.rm=T)
max_MYOH_vol <- max(MYOH$Liquid, na.rm=T)
min_MYOH_vol <- min(MYOH$Liquid, na.rm=T)
#Tabel untuk Return MYOH
DStat_MYOH_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_MYOH_dret,median_MYOH_dret,sd_MYOH_dret,max_MYOH_dret,min_MYOH_dret)
)
DStat_MYOH_dret#Tabel untuk Volume MYOH
DStat_MYOH_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_MYOH_vol,median_MYOH_vol,sd_MYOH_vol,max_MYOH_vol,min_MYOH_vol)
)
DStat_MYOH_vol#Daily Return of PANR
avg_PANR_dret <- mean(PANR$dret, na.rm=T)
median_PANR_dret <- median(PANR$dret, na.rm=T)
sd_PANR_dret <- sd(PANR$dret, na.rm=T)
max_PANR_dret <- max(PANR$dret, na.rm=T)
min_PANR_dret <- min(PANR$dret, na.rm=T)
#Volume of PANR
avg_PANR_vol <- mean(PANR$Liquid, na.rm=T)
median_PANR_vol <- median(PANR$Liquid, na.rm=T)
sd_PANR_vol <- sd(PANR$Liquid, na.rm=T)
max_PANR_vol <- max(PANR$Liquid, na.rm=T)
min_PANR_vol <- min(PANR$Liquid, na.rm=T)
#Tabel untuk Return PANR
DStat_PANR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PANR_dret,median_PANR_dret,sd_PANR_dret,max_PANR_dret,min_PANR_dret)
)
DStat_PANR_dret#Tabel untuk Volume PANR
DStat_PANR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PANR_vol,median_PANR_vol,sd_PANR_vol,max_PANR_vol,min_PANR_vol)
)
DStat_PANR_vol#Daily Return of PGAS
avg_PGAS_dret <- mean(PGAS$dret, na.rm=T)
median_PGAS_dret <- median(PGAS$dret, na.rm=T)
sd_PGAS_dret <- sd(PGAS$dret, na.rm=T)
max_PGAS_dret <- max(PGAS$dret, na.rm=T)
min_PGAS_dret <- min(PGAS$dret, na.rm=T)
#Volume of PGAS
avg_PGAS_vol <- mean(PGAS$Liquid, na.rm=T)
median_PGAS_vol <- median(PGAS$Liquid, na.rm=T)
sd_PGAS_vol <- sd(PGAS$Liquid, na.rm=T)
max_PGAS_vol <- max(PGAS$Liquid, na.rm=T)
min_PGAS_vol <- min(PGAS$Liquid, na.rm=T)
#Tabel untuk Return PGAS
DStat_PGAS_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PGAS_dret,median_PGAS_dret,sd_PGAS_dret,max_PGAS_dret,min_PGAS_dret)
)
DStat_PGAS_dret#Tabel untuk Volume PGAS
DStat_PGAS_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PGAS_vol,median_PGAS_vol,sd_PGAS_vol,max_PGAS_vol,min_PGAS_vol)
)
DStat_PGAS_vol#Daily Return of PICO
avg_PICO_dret <- mean(PICO$dret, na.rm=T)
median_PICO_dret <- median(PICO$dret, na.rm=T)
sd_PICO_dret <- sd(PICO$dret, na.rm=T)
max_PICO_dret <- max(PICO$dret, na.rm=T)
min_PICO_dret <- min(PICO$dret, na.rm=T)
#Volume of PICO
avg_PICO_vol <- mean(PICO$Liquid, na.rm=T)
median_PICO_vol <- median(PICO$Liquid, na.rm=T)
sd_PICO_vol <- sd(PICO$Liquid, na.rm=T)
max_PICO_vol <- max(PICO$Liquid, na.rm=T)
min_PICO_vol <- min(PICO$Liquid, na.rm=T)
#Tabel untuk Return PICO
DStat_PICO_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PICO_dret,median_PICO_dret,sd_PICO_dret,max_PICO_dret,min_PICO_dret)
)
DStat_PICO_dret#Tabel untuk Volume PICO
DStat_PICO_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PICO_vol,median_PICO_vol,sd_PICO_vol,max_PICO_vol,min_PICO_vol)
)
DStat_PICO_vol#Daily Return of PJAA
avg_PJAA_dret <- mean(PJAA$dret, na.rm=T)
median_PJAA_dret <- median(PJAA$dret, na.rm=T)
sd_PJAA_dret <- sd(PJAA$dret, na.rm=T)
max_PJAA_dret <- max(PJAA$dret, na.rm=T)
min_PJAA_dret <- min(PJAA$dret, na.rm=T)
#Volume of PJAA
avg_PJAA_vol <- mean(PJAA$Liquid, na.rm=T)
median_PJAA_vol <- median(PJAA$Liquid, na.rm=T)
sd_PJAA_vol <- sd(PJAA$Liquid, na.rm=T)
max_PJAA_vol <- max(PJAA$Liquid, na.rm=T)
min_PJAA_vol <- min(PJAA$Liquid, na.rm=T)
#Tabel untuk Return PJAA
DStat_PJAA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PJAA_dret,median_PJAA_dret,sd_PJAA_dret,max_PJAA_dret,min_PJAA_dret)
)
DStat_PJAA_dret#Tabel untuk Volume PJAA
DStat_PJAA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PJAA_vol,median_PJAA_vol,sd_PJAA_vol,max_PJAA_vol,min_PJAA_vol)
)
DStat_PJAA_vol#Daily Return of PNBN
avg_PNBN_dret <- mean(PNBN$dret, na.rm=T)
median_PNBN_dret <- median(PNBN$dret, na.rm=T)
sd_PNBN_dret <- sd(PNBN$dret, na.rm=T)
max_PNBN_dret <- max(PNBN$dret, na.rm=T)
min_PNBN_dret <- min(PNBN$dret, na.rm=T)
#Volume of PNBN
avg_PNBN_vol <- mean(PNBN$Liquid, na.rm=T)
median_PNBN_vol <- median(PNBN$Liquid, na.rm=T)
sd_PNBN_vol <- sd(PNBN$Liquid, na.rm=T)
max_PNBN_vol <- max(PNBN$Liquid, na.rm=T)
min_PNBN_vol <- min(PNBN$Liquid, na.rm=T)
#Tabel untuk Return PNBN
DStat_PNBN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PNBN_dret,median_PNBN_dret,sd_PNBN_dret,max_PNBN_dret,min_PNBN_dret)
)
DStat_PNBN_dret#Tabel untuk Volume PNBN
DStat_PNBN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PNBN_vol,median_PNBN_vol,sd_PNBN_vol,max_PNBN_vol,min_PNBN_vol)
)
DStat_PNBN_vol#Daily Return of PNIN
avg_PNIN_dret <- mean(PNIN$dret, na.rm=T)
median_PNIN_dret <- median(PNIN$dret, na.rm=T)
sd_PNIN_dret <- sd(PNIN$dret, na.rm=T)
max_PNIN_dret <- max(PNIN$dret, na.rm=T)
min_PNIN_dret <- min(PNIN$dret, na.rm=T)
#Volume of PNIN
avg_PNIN_vol <- mean(PNIN$Liquid, na.rm=T)
median_PNIN_vol <- median(PNIN$Liquid, na.rm=T)
sd_PNIN_vol <- sd(PNIN$Liquid, na.rm=T)
max_PNIN_vol <- max(PNIN$Liquid, na.rm=T)
min_PNIN_vol <- min(PNIN$Liquid, na.rm=T)
#Tabel untuk Return PNIN
DStat_PNIN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PNIN_dret,median_PNIN_dret,sd_PNIN_dret,max_PNIN_dret,min_PNIN_dret)
)
DStat_PNIN_dret#Tabel untuk Volume PNIN
DStat_PNIN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PNIN_vol,median_PNIN_vol,sd_PNIN_vol,max_PNIN_vol,min_PNIN_vol)
)
DStat_PNIN_vol#Daily Return of PTPP
avg_PTPP_dret <- mean(PTPP$dret, na.rm=T)
median_PTPP_dret <- median(PTPP$dret, na.rm=T)
sd_PTPP_dret <- sd(PTPP$dret, na.rm=T)
max_PTPP_dret <- max(PTPP$dret, na.rm=T)
min_PTPP_dret <- min(PTPP$dret, na.rm=T)
#Volume of PTPP
avg_PTPP_vol <- mean(PTPP$Liquid, na.rm=T)
median_PTPP_vol <- median(PTPP$Liquid, na.rm=T)
sd_PTPP_vol <- sd(PTPP$Liquid, na.rm=T)
max_PTPP_vol <- max(PTPP$Liquid, na.rm=T)
min_PTPP_vol <- min(PTPP$Liquid, na.rm=T)
#Tabel untuk Return PTPP
DStat_PTPP_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PTPP_dret,median_PTPP_dret,sd_PTPP_dret,max_PTPP_dret,min_PTPP_dret)
)
DStat_PTPP_dret#Tabel untuk Volume PTPP
DStat_PTPP_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PTPP_vol,median_PTPP_vol,sd_PTPP_vol,max_PTPP_vol,min_PTPP_vol)
)
DStat_PTPP_vol#Daily Return of PTSN
avg_PTSN_dret <- mean(PTSN$dret, na.rm=T)
median_PTSN_dret <- median(PTSN$dret, na.rm=T)
sd_PTSN_dret <- sd(PTSN$dret, na.rm=T)
max_PTSN_dret <- max(PTSN$dret, na.rm=T)
min_PTSN_dret <- min(PTSN$dret, na.rm=T)
#Volume of PTSN
avg_PTSN_vol <- mean(PTSN$Liquid, na.rm=T)
median_PTSN_vol <- median(PTSN$Liquid, na.rm=T)
sd_PTSN_vol <- sd(PTSN$Liquid, na.rm=T)
max_PTSN_vol <- max(PTSN$Liquid, na.rm=T)
min_PTSN_vol <- min(PTSN$Liquid, na.rm=T)
#Tabel untuk Return PTSN
DStat_PTSN_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PTSN_dret,median_PTSN_dret,sd_PTSN_dret,max_PTSN_dret,min_PTSN_dret)
)
DStat_PTSN_dret#Tabel untuk Volume PTSN
DStat_PTSN_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PTSN_vol,median_PTSN_vol,sd_PTSN_vol,max_PTSN_vol,min_PTSN_vol)
)
DStat_PTSN_vol#Daily Return of PWON
avg_PWON_dret <- mean(PWON$dret, na.rm=T)
median_PWON_dret <- median(PWON$dret, na.rm=T)
sd_PWON_dret <- sd(PWON$dret, na.rm=T)
max_PWON_dret <- max(PWON$dret, na.rm=T)
min_PWON_dret <- min(PWON$dret, na.rm=T)
#Volume of PWON
avg_PWON_vol <- mean(PWON$Liquid, na.rm=T)
median_PWON_vol <- median(PWON$Liquid, na.rm=T)
sd_PWON_vol <- sd(PWON$Liquid, na.rm=T)
max_PWON_vol <- max(PWON$Liquid, na.rm=T)
min_PWON_vol <- min(PWON$Liquid, na.rm=T)
#Tabel untuk Return PWON
DStat_PWON_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PWON_dret,median_PWON_dret,sd_PWON_dret,max_PWON_dret,min_PWON_dret)
)
DStat_PWON_dret#Tabel untuk Volume PWON
DStat_PWON_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PWON_vol,median_PWON_vol,sd_PWON_vol,max_PWON_vol,min_PWON_vol)
)
DStat_PWON_vol#Daily Return of PYFA
avg_PYFA_dret <- mean(PYFA$dret, na.rm=T)
median_PYFA_dret <- median(PYFA$dret, na.rm=T)
sd_PYFA_dret <- sd(PYFA$dret, na.rm=T)
max_PYFA_dret <- max(PYFA$dret, na.rm=T)
min_PYFA_dret <- min(PYFA$dret, na.rm=T)
#Volume of PYFA
avg_PYFA_vol <- mean(PYFA$Liquid, na.rm=T)
median_PYFA_vol <- median(PYFA$Liquid, na.rm=T)
sd_PYFA_vol <- sd(PYFA$Liquid, na.rm=T)
max_PYFA_vol <- max(PYFA$Liquid, na.rm=T)
min_PYFA_vol <- min(PYFA$Liquid, na.rm=T)
#Tabel untuk Return PYFA
DStat_PYFA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PYFA_dret,median_PYFA_dret,sd_PYFA_dret,max_PYFA_dret,min_PYFA_dret)
)
DStat_PYFA_dret#Tabel untuk Volume PYFA
DStat_PYFA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PYFA_vol,median_PYFA_vol,sd_PYFA_vol,max_PYFA_vol,min_PYFA_vol)
)
DStat_PYFA_vol#Daily Return of PYFA
avg_PYFA_dret <- mean(PYFA$dret, na.rm=T)
median_PYFA_dret <- median(PYFA$dret, na.rm=T)
sd_PYFA_dret <- sd(PYFA$dret, na.rm=T)
max_PYFA_dret <- max(PYFA$dret, na.rm=T)
min_PYFA_dret <- min(PYFA$dret, na.rm=T)
#Volume of PYFA
avg_PYFA_vol <- mean(PYFA$Liquid, na.rm=T)
median_PYFA_vol <- median(PYFA$Liquid, na.rm=T)
sd_PYFA_vol <- sd(PYFA$Liquid, na.rm=T)
max_PYFA_vol <- max(PYFA$Liquid, na.rm=T)
min_PYFA_vol <- min(PYFA$Liquid, na.rm=T)
#Tabel untuk Return PYFA
DStat_PYFA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_PYFA_dret,median_PYFA_dret,sd_PYFA_dret,max_PYFA_dret,min_PYFA_dret)
)
DStat_PYFA_dret#Tabel untuk Volume PYFA
DStat_PYFA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_PYFA_vol,median_PYFA_vol,sd_PYFA_vol,max_PYFA_vol,min_PYFA_vol)
)
DStat_PYFA_vol#Daily Return of RICY
avg_RICY_dret <- mean(RICY$dret, na.rm=T)
median_RICY_dret <- median(RICY$dret, na.rm=T)
sd_RICY_dret <- sd(RICY$dret, na.rm=T)
max_RICY_dret <- max(RICY$dret, na.rm=T)
min_RICY_dret <- min(RICY$dret, na.rm=T)
#Volume of RICY
avg_RICY_vol <- mean(RICY$Liquid, na.rm=T)
median_RICY_vol <- median(RICY$Liquid, na.rm=T)
sd_RICY_vol <- sd(RICY$Liquid, na.rm=T)
max_RICY_vol <- max(RICY$Liquid, na.rm=T)
min_RICY_vol <- min(RICY$Liquid, na.rm=T)
#Tabel untuk Return RICY
DStat_RICY_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_RICY_dret,median_RICY_dret,sd_RICY_dret,max_RICY_dret,min_RICY_dret)
)
DStat_RICY_dret#Tabel untuk Volume RICY
DStat_RICY_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_RICY_vol,median_RICY_vol,sd_RICY_vol,max_RICY_vol,min_RICY_vol)
)
DStat_RICY_vol#Daily Return of SAFE
avg_SAFE_dret <- mean(SAFE$dret, na.rm=T)
median_SAFE_dret <- median(SAFE$dret, na.rm=T)
sd_SAFE_dret <- sd(SAFE$dret, na.rm=T)
max_SAFE_dret <- max(SAFE$dret, na.rm=T)
min_SAFE_dret <- min(SAFE$dret, na.rm=T)
#Volume of SAFE
avg_SAFE_vol <- mean(SAFE$Liquid, na.rm=T)
median_SAFE_vol <- median(SAFE$Liquid, na.rm=T)
sd_SAFE_vol <- sd(SAFE$Liquid, na.rm=T)
max_SAFE_vol <- max(SAFE$Liquid, na.rm=T)
min_SAFE_vol <- min(SAFE$Liquid, na.rm=T)
#Tabel untuk Return SAFE
DStat_SAFE_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_SAFE_dret,median_SAFE_dret,sd_SAFE_dret,max_SAFE_dret,min_SAFE_dret)
)
DStat_SAFE_dret#Tabel untuk Volume SAFE
DStat_SAFE_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_SAFE_vol,median_SAFE_vol,sd_SAFE_vol,max_SAFE_vol,min_SAFE_vol)
)
DStat_SAFE_vol#Daily Return of SDMU
avg_SDMU_dret <- mean(SDMU$dret, na.rm=T)
median_SDMU_dret <- median(SDMU$dret, na.rm=T)
sd_SDMU_dret <- sd(SDMU$dret, na.rm=T)
max_SDMU_dret <- max(SDMU$dret, na.rm=T)
min_SDMU_dret <- min(SDMU$dret, na.rm=T)
#Volume of SDMU
avg_SDMU_vol <- mean(SDMU$Liquid, na.rm=T)
median_SDMU_vol <- median(SDMU$Liquid, na.rm=T)
sd_SDMU_vol <- sd(SDMU$Liquid, na.rm=T)
max_SDMU_vol <- max(SDMU$Liquid, na.rm=T)
min_SDMU_vol <- min(SDMU$Liquid, na.rm=T)
#Tabel untuk Return SDMU
DStat_SDMU_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_SDMU_dret,median_SDMU_dret,sd_SDMU_dret,max_SDMU_dret,min_SDMU_dret)
)
DStat_SDMU_dret#Tabel untuk Volume SDMU
DStat_SDMU_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_SDMU_vol,median_SDMU_vol,sd_SDMU_vol,max_SDMU_vol,min_SDMU_vol)
)
DStat_SDMU_vol#Daily Return of SHID
avg_SHID_dret <- mean(SHID$dret, na.rm=T)
median_SHID_dret <- median(SHID$dret, na.rm=T)
sd_SHID_dret <- sd(SHID$dret, na.rm=T)
max_SHID_dret <- max(SHID$dret, na.rm=T)
min_SHID_dret <- min(SHID$dret, na.rm=T)
#Volume of SHID
avg_SHID_vol <- mean(SHID$Liquid, na.rm=T)
median_SHID_vol <- median(SHID$Liquid, na.rm=T)
sd_SHID_vol <- sd(SHID$Liquid, na.rm=T)
max_SHID_vol <- max(SHID$Liquid, na.rm=T)
min_SHID_vol <- min(SHID$Liquid, na.rm=T)
#Tabel untuk Return SHID
DStat_SHID_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_SHID_dret,median_SHID_dret,sd_SHID_dret,max_SHID_dret,min_SHID_dret)
)
DStat_SHID_dret#Tabel untuk Volume SHID
DStat_SHID_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_SHID_vol,median_SHID_vol,sd_SHID_vol,max_SHID_vol,min_SHID_vol)
)
DStat_SHID_vol#Daily Return of SMDM
avg_SMDM_dret <- mean(SMDM$dret, na.rm=T)
median_SMDM_dret <- median(SMDM$dret, na.rm=T)
sd_SMDM_dret <- sd(SMDM$dret, na.rm=T)
max_SMDM_dret <- max(SMDM$dret, na.rm=T)
min_SMDM_dret <- min(SMDM$dret, na.rm=T)
#Volume of SMDM
avg_SMDM_vol <- mean(SMDM$Liquid, na.rm=T)
median_SMDM_vol <- median(SMDM$Liquid, na.rm=T)
sd_SMDM_vol <- sd(SMDM$Liquid, na.rm=T)
max_SMDM_vol <- max(SMDM$Liquid, na.rm=T)
min_SMDM_vol <- min(SMDM$Liquid, na.rm=T)
#Tabel untuk Return SMDM
DStat_SMDM_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_SMDM_dret,median_SMDM_dret,sd_SMDM_dret,max_SMDM_dret,min_SMDM_dret)
)
DStat_SMDM_dret#Tabel untuk Volume SMDM
DStat_SMDM_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_SMDM_vol,median_SMDM_vol,sd_SMDM_vol,max_SMDM_vol,min_SMDM_vol)
)
DStat_SMDM_vol#Daily Return of SMDR
avg_SMDR_dret <- mean(SMDR$dret, na.rm=T)
median_SMDR_dret <- median(SMDR$dret, na.rm=T)
sd_SMDR_dret <- sd(SMDR$dret, na.rm=T)
max_SMDR_dret <- max(SMDR$dret, na.rm=T)
min_SMDR_dret <- min(SMDR$dret, na.rm=T)
#Volume of SMDR
avg_SMDR_vol <- mean(SMDR$Liquid, na.rm=T)
median_SMDR_vol <- median(SMDR$Liquid, na.rm=T)
sd_SMDR_vol <- sd(SMDR$Liquid, na.rm=T)
max_SMDR_vol <- max(SMDR$Liquid, na.rm=T)
min_SMDR_vol <- min(SMDR$Liquid, na.rm=T)
#Tabel untuk Return SMDR
DStat_SMDR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_SMDR_dret,median_SMDR_dret,sd_SMDR_dret,max_SMDR_dret,min_SMDR_dret)
)
DStat_SMDR_dret#Tabel untuk Volume SMDR
DStat_SMDR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_SMDR_vol,median_SMDR_vol,sd_SMDR_vol,max_SMDR_vol,min_SMDR_vol)
)
DStat_SMDR_vol#Daily Return of SMGR
avg_SMGR_dret <- mean(SMGR$dret, na.rm=T)
median_SMGR_dret <- median(SMGR$dret, na.rm=T)
sd_SMGR_dret <- sd(SMGR$dret, na.rm=T)
max_SMGR_dret <- max(SMGR$dret, na.rm=T)
min_SMGR_dret <- min(SMGR$dret, na.rm=T)
#Volume of SMGR
avg_SMGR_vol <- mean(SMGR$Liquid, na.rm=T)
median_SMGR_vol <- median(SMGR$Liquid, na.rm=T)
sd_SMGR_vol <- sd(SMGR$Liquid, na.rm=T)
max_SMGR_vol <- max(SMGR$Liquid, na.rm=T)
min_SMGR_vol <- min(SMGR$Liquid, na.rm=T)
#Tabel untuk Return SMGR
DStat_SMGR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_SMGR_dret,median_SMGR_dret,sd_SMGR_dret,max_SMGR_dret,min_SMGR_dret)
)
DStat_SMGR_dret#Tabel untuk Volume SMGR
DStat_SMGR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_SMGR_vol,median_SMGR_vol,sd_SMGR_vol,max_SMGR_vol,min_SMGR_vol)
)
DStat_SMGR_vol#Daily Return of SRAJ
avg_SRAJ_dret <- mean(SRAJ$dret, na.rm=T)
median_SRAJ_dret <- median(SRAJ$dret, na.rm=T)
sd_SRAJ_dret <- sd(SRAJ$dret, na.rm=T)
max_SRAJ_dret <- max(SRAJ$dret, na.rm=T)
min_SRAJ_dret <- min(SRAJ$dret, na.rm=T)
#Volume of SRAJ
avg_SRAJ_vol <- mean(SRAJ$Liquid, na.rm=T)
median_SRAJ_vol <- median(SRAJ$Liquid, na.rm=T)
sd_SRAJ_vol <- sd(SRAJ$Liquid, na.rm=T)
max_SRAJ_vol <- max(SRAJ$Liquid, na.rm=T)
min_SRAJ_vol <- min(SRAJ$Liquid, na.rm=T)
#Tabel untuk Return SRAJ
DStat_SRAJ_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_SRAJ_dret,median_SRAJ_dret,sd_SRAJ_dret,max_SRAJ_dret,min_SRAJ_dret)
)
DStat_SRAJ_dret#Tabel untuk Volume SRAJ
DStat_SRAJ_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_SRAJ_vol,median_SRAJ_vol,sd_SRAJ_vol,max_SRAJ_vol,min_SRAJ_vol)
)
DStat_SRAJ_vol#Daily Return of STTP
avg_STTP_dret <- mean(STTP$dret, na.rm=T)
median_STTP_dret <- median(STTP$dret, na.rm=T)
sd_STTP_dret <- sd(STTP$dret, na.rm=T)
max_STTP_dret <- max(STTP$dret, na.rm=T)
min_STTP_dret <- min(STTP$dret, na.rm=T)
#Volume of STTP
avg_STTP_vol <- mean(STTP$Liquid, na.rm=T)
median_STTP_vol <- median(STTP$Liquid, na.rm=T)
sd_STTP_vol <- sd(STTP$Liquid, na.rm=T)
max_STTP_vol <- max(STTP$Liquid, na.rm=T)
min_STTP_vol <- min(STTP$Liquid, na.rm=T)
#Tabel untuk Return STTP
DStat_STTP_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_STTP_dret,median_STTP_dret,sd_STTP_dret,max_STTP_dret,min_STTP_dret)
)
DStat_STTP_dret#Tabel untuk Volume STTP
DStat_STTP_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_STTP_vol,median_STTP_vol,sd_STTP_vol,max_STTP_vol,min_STTP_vol)
)
DStat_STTP_vol#Daily Return of TBLA
avg_TBLA_dret <- mean(TBLA$dret, na.rm=T)
median_TBLA_dret <- median(TBLA$dret, na.rm=T)
sd_TBLA_dret <- sd(TBLA$dret, na.rm=T)
max_TBLA_dret <- max(TBLA$dret, na.rm=T)
min_TBLA_dret <- min(TBLA$dret, na.rm=T)
#Volume of TBLA
avg_TBLA_vol <- mean(TBLA$Liquid, na.rm=T)
median_TBLA_vol <- median(TBLA$Liquid, na.rm=T)
sd_TBLA_vol <- sd(TBLA$Liquid, na.rm=T)
max_TBLA_vol <- max(TBLA$Liquid, na.rm=T)
min_TBLA_vol <- min(TBLA$Liquid, na.rm=T)
#Tabel untuk Return TBLA
DStat_TBLA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_TBLA_dret,median_TBLA_dret,sd_TBLA_dret,max_TBLA_dret,min_TBLA_dret)
)
DStat_TBLA_dret#Tabel untuk Volume TBLA
DStat_TBLA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_TBLA_vol,median_TBLA_vol,sd_TBLA_vol,max_TBLA_vol,min_TBLA_vol)
)
DStat_TBLA_vol#Daily Return of TLKM
avg_TLKM_dret <- mean(TLKM$dret, na.rm=T)
median_TLKM_dret <- median(TLKM$dret, na.rm=T)
sd_TLKM_dret <- sd(TLKM$dret, na.rm=T)
max_TLKM_dret <- max(TLKM$dret, na.rm=T)
min_TLKM_dret <- min(TLKM$dret, na.rm=T)
#Volume of TLKM
avg_TLKM_vol <- mean(TLKM$Liquid, na.rm=T)
median_TLKM_vol <- median(TLKM$Liquid, na.rm=T)
sd_TLKM_vol <- sd(TLKM$Liquid, na.rm=T)
max_TLKM_vol <- max(TLKM$Liquid, na.rm=T)
min_TLKM_vol <- min(TLKM$Liquid, na.rm=T)
#Tabel untuk Return TLKM
DStat_TLKM_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_TLKM_dret,median_TLKM_dret,sd_TLKM_dret,max_TLKM_dret,min_TLKM_dret)
)
DStat_TLKM_dret#Tabel untuk Volume TLKM
DStat_TLKM_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_TLKM_vol,median_TLKM_vol,sd_TLKM_vol,max_TLKM_vol,min_TLKM_vol)
)
DStat_TLKM_vol#Daily Return of TMAS
avg_TMAS_dret <- mean(TMAS$dret, na.rm=T)
median_TMAS_dret <- median(TMAS$dret, na.rm=T)
sd_TMAS_dret <- sd(TMAS$dret, na.rm=T)
max_TMAS_dret <- max(TMAS$dret, na.rm=T)
min_TMAS_dret <- min(TMAS$dret, na.rm=T)
#Volume of TMAS
avg_TMAS_vol <- mean(TMAS$Liquid, na.rm=T)
median_TMAS_vol <- median(TMAS$Liquid, na.rm=T)
sd_TMAS_vol <- sd(TMAS$Liquid, na.rm=T)
max_TMAS_vol <- max(TMAS$Liquid, na.rm=T)
min_TMAS_vol <- min(TMAS$Liquid, na.rm=T)
#Tabel untuk Return TMAS
DStat_TMAS_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_TMAS_dret,median_TMAS_dret,sd_TMAS_dret,max_TMAS_dret,min_TMAS_dret)
)
DStat_TMAS_dret#Tabel untuk Volume TMAS
DStat_TMAS_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_TMAS_vol,median_TMAS_vol,sd_TMAS_vol,max_TMAS_vol,min_TMAS_vol)
)
DStat_TMAS_vol#Daily Return of TOWR
avg_TOWR_dret <- mean(TOWR$dret, na.rm=T)
median_TOWR_dret <- median(TOWR$dret, na.rm=T)
sd_TOWR_dret <- sd(TOWR$dret, na.rm=T)
max_TOWR_dret <- max(TOWR$dret, na.rm=T)
min_TOWR_dret <- min(TOWR$dret, na.rm=T)
#Volume of TOWR
avg_TOWR_vol <- mean(TOWR$Liquid, na.rm=T)
median_TOWR_vol <- median(TOWR$Liquid, na.rm=T)
sd_TOWR_vol <- sd(TOWR$Liquid, na.rm=T)
max_TOWR_vol <- max(TOWR$Liquid, na.rm=T)
min_TOWR_vol <- min(TOWR$Liquid, na.rm=T)
#Tabel untuk Return TOWR
DStat_TOWR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_TOWR_dret,median_TOWR_dret,sd_TOWR_dret,max_TOWR_dret,min_TOWR_dret)
)
DStat_TOWR_dret#Tabel untuk Volume TOWR
DStat_TOWR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_TOWR_vol,median_TOWR_vol,sd_TOWR_vol,max_TOWR_vol,min_TOWR_vol)
)
DStat_TOWR_vol#Daily Return of TSPC
avg_TSPC_dret <- mean(TSPC$dret, na.rm=T)
median_TSPC_dret <- median(TSPC$dret, na.rm=T)
sd_TSPC_dret <- sd(TSPC$dret, na.rm=T)
max_TSPC_dret <- max(TSPC$dret, na.rm=T)
min_TSPC_dret <- min(TSPC$dret, na.rm=T)
#Volume of TSPC
avg_TSPC_vol <- mean(TSPC$Liquid, na.rm=T)
median_TSPC_vol <- median(TSPC$Liquid, na.rm=T)
sd_TSPC_vol <- sd(TSPC$Liquid, na.rm=T)
max_TSPC_vol <- max(TSPC$Liquid, na.rm=T)
min_TSPC_vol <- min(TSPC$Liquid, na.rm=T)
#Tabel untuk Return TSPC
DStat_TSPC_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_TSPC_dret,median_TSPC_dret,sd_TSPC_dret,max_TSPC_dret,min_TSPC_dret)
)
DStat_TSPC_dret#Tabel untuk Volume TSPC
DStat_TSPC_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_TSPC_vol,median_TSPC_vol,sd_TSPC_vol,max_TSPC_vol,min_TSPC_vol)
)
DStat_TSPC_vol#Daily Return of UNTR
avg_UNTR_dret <- mean(UNTR$dret, na.rm=T)
median_UNTR_dret <- median(UNTR$dret, na.rm=T)
sd_UNTR_dret <- sd(UNTR$dret, na.rm=T)
max_UNTR_dret <- max(UNTR$dret, na.rm=T)
min_UNTR_dret <- min(UNTR$dret, na.rm=T)
#Volume of UNTR
avg_UNTR_vol <- mean(UNTR$Liquid, na.rm=T)
median_UNTR_vol <- median(UNTR$Liquid, na.rm=T)
sd_UNTR_vol <- sd(UNTR$Liquid, na.rm=T)
max_UNTR_vol <- max(UNTR$Liquid, na.rm=T)
min_UNTR_vol <- min(UNTR$Liquid, na.rm=T)
#Tabel untuk Return UNTR
DStat_UNTR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_UNTR_dret,median_UNTR_dret,sd_UNTR_dret,max_UNTR_dret,min_UNTR_dret)
)
DStat_UNTR_dret#Tabel untuk Volume UNTR
DStat_UNTR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_UNTR_vol,median_UNTR_vol,sd_UNTR_vol,max_UNTR_vol,min_UNTR_vol)
)
DStat_UNTR_vol#Daily Return of UNVR
avg_UNVR_dret <- mean(UNVR$dret, na.rm=T)
median_UNVR_dret <- median(UNVR$dret, na.rm=T)
sd_UNVR_dret <- sd(UNVR$dret, na.rm=T)
max_UNVR_dret <- max(UNVR$dret, na.rm=T)
min_UNVR_dret <- min(UNVR$dret, na.rm=T)
#Volume of UNVR
avg_UNVR_vol <- mean(UNVR$Liquid, na.rm=T)
median_UNVR_vol <- median(UNVR$Liquid, na.rm=T)
sd_UNVR_vol <- sd(UNVR$Liquid, na.rm=T)
max_UNVR_vol <- max(UNVR$Liquid, na.rm=T)
min_UNVR_vol <- min(UNVR$Liquid, na.rm=T)
#Tabel untuk Return UNVR
DStat_UNVR_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_UNVR_dret,median_UNVR_dret,sd_UNVR_dret,max_UNVR_dret,min_UNVR_dret)
)
DStat_UNVR_dret#Tabel untuk Volume UNVR
DStat_UNVR_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_UNVR_vol,median_UNVR_vol,sd_UNVR_vol,max_UNVR_vol,min_UNVR_vol)
)
DStat_UNVR_vol#Daily Return of WEHA
avg_WEHA_dret <- mean(WEHA$dret, na.rm=T)
median_WEHA_dret <- median(WEHA$dret, na.rm=T)
sd_WEHA_dret <- sd(WEHA$dret, na.rm=T)
max_WEHA_dret <- max(WEHA$dret, na.rm=T)
min_WEHA_dret <- min(WEHA$dret, na.rm=T)
#Volume of WEHA
avg_WEHA_vol <- mean(WEHA$Liquid, na.rm=T)
median_WEHA_vol <- median(WEHA$Liquid, na.rm=T)
sd_WEHA_vol <- sd(WEHA$Liquid, na.rm=T)
max_WEHA_vol <- max(WEHA$Liquid, na.rm=T)
min_WEHA_vol <- min(WEHA$Liquid, na.rm=T)
#Tabel untuk Return WEHA
DStat_WEHA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_WEHA_dret,median_WEHA_dret,sd_WEHA_dret,max_WEHA_dret,min_WEHA_dret)
)
DStat_WEHA_dret#Tabel untuk Volume WEHA
DStat_WEHA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_WEHA_vol,median_WEHA_vol,sd_WEHA_vol,max_WEHA_vol,min_WEHA_vol)
)
DStat_WEHA_vol#Daily Return of WINS
avg_WINS_dret <- mean(WINS$dret, na.rm=T)
median_WINS_dret <- median(WINS$dret, na.rm=T)
sd_WINS_dret <- sd(WINS$dret, na.rm=T)
max_WINS_dret <- max(WINS$dret, na.rm=T)
min_WINS_dret <- min(WINS$dret, na.rm=T)
#Volume of WINS
avg_WINS_vol <- mean(WINS$Liquid, na.rm=T)
median_WINS_vol <- median(WINS$Liquid, na.rm=T)
sd_WINS_vol <- sd(WINS$Liquid, na.rm=T)
max_WINS_vol <- max(WINS$Liquid, na.rm=T)
min_WINS_vol <- min(WINS$Liquid, na.rm=T)
#Tabel untuk Return WINS
DStat_WINS_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_WINS_dret,median_WINS_dret,sd_WINS_dret,max_WINS_dret,min_WINS_dret)
)
DStat_WINS_dret#Tabel untuk Volume WINS
DStat_WINS_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_WINS_vol,median_WINS_vol,sd_WINS_vol,max_WINS_vol,min_WINS_vol)
)
DStat_WINS_vol#Daily Return of YPAS
avg_YPAS_dret <- mean(YPAS$dret, na.rm=T)
median_YPAS_dret <- median(YPAS$dret, na.rm=T)
sd_YPAS_dret <- sd(YPAS$dret, na.rm=T)
max_YPAS_dret <- max(YPAS$dret, na.rm=T)
min_YPAS_dret <- min(YPAS$dret, na.rm=T)
#Volume of YPAS
avg_YPAS_vol <- mean(YPAS$Liquid, na.rm=T)
median_YPAS_vol <- median(YPAS$Liquid, na.rm=T)
sd_YPAS_vol <- sd(YPAS$Liquid, na.rm=T)
max_YPAS_vol <- max(YPAS$Liquid, na.rm=T)
min_YPAS_vol <- min(YPAS$Liquid, na.rm=T)
#Tabel untuk Return YPAS
DStat_YPAS_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_YPAS_dret,median_YPAS_dret,sd_YPAS_dret,max_YPAS_dret,min_YPAS_dret)
)
DStat_YPAS_dret#Tabel untuk Volume YPAS
DStat_YPAS_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_YPAS_vol,median_YPAS_vol,sd_YPAS_vol,max_YPAS_vol,min_YPAS_vol)
)
DStat_YPAS_vol#Daily Return of ZBRA
avg_ZBRA_dret <- mean(ZBRA$dret, na.rm=T)
median_ZBRA_dret <- median(ZBRA$dret, na.rm=T)
sd_ZBRA_dret <- sd(ZBRA$dret, na.rm=T)
max_ZBRA_dret <- max(ZBRA$dret, na.rm=T)
min_ZBRA_dret <- min(ZBRA$dret, na.rm=T)
#Volume of ZBRA
avg_ZBRA_vol <- mean(ZBRA$Liquid, na.rm=T)
median_ZBRA_vol <- median(ZBRA$Liquid, na.rm=T)
sd_ZBRA_vol <- sd(ZBRA$Liquid, na.rm=T)
max_ZBRA_vol <- max(ZBRA$Liquid, na.rm=T)
min_ZBRA_vol <- min(ZBRA$Liquid, na.rm=T)
#Tabel untuk Return ZBRA
DStat_ZBRA_dret <- data.frame(
Description=c("Average Daily Return","Median of Daily Return","Standard Deviation of Daily Return","Max Daily Return","Minimum Daily Return"),
Values=c(avg_ZBRA_dret,median_ZBRA_dret,sd_ZBRA_dret,max_ZBRA_dret,min_ZBRA_dret)
)
DStat_ZBRA_dret#Tabel untuk Volume ZBRA
DStat_ZBRA_vol <- data.frame(
Description=c("Average Daily Volume","Median of Daily Volume","Standard Deviation of Daily Volume","Max Daily Volume","Minimum Daily Volume"),
Values=c(avg_ZBRA_vol,median_ZBRA_vol,sd_ZBRA_vol,max_ZBRA_vol,min_ZBRA_vol)
)
DStat_ZBRA_volSetelah menemukan descriptive statistic untuk variable Daily Return dan Liquidity, langkah terakhir yang perlu dilakukan adalah untuk melakukan korelasi antara kedua variable tersebut. Karena data-data saham terpisah, maka sebelumnya perlu dilakukan penggabungan terhadap semua data tersebut. Ini juga dilakukan agar hanya ada 1 hasil korelasi yang muncul. Selain itu, parameter use=“complete.obs” digunakan untuk memastikan nilai-nilai NA yang bisa saja ada tidak menyebabkan error pada hasil perhitungan korelasi.
all_stocks <- rbind(AALI,ABBA,ABMM,ACES,ADES,ADHI,ADMG,ADRO,AGRO,AISA,AKPI,AKRA,AKSI,ALDO,ALMI,AMFG, AMRT,ANTM,APLN,ASII,ASRI,AUTO,BABP,BAPA,BAYU,BBCA,BBRI,BBTN,BDMN,BFIN,BHIT,BIPP,BISI,BKDP,BKSL,BMRI,BMTR,BNBA,BNGA,BSDE,BTON,BULL,BUMI,CASS,CEKA,CENT,CMNP,CPIN,CTRA,DART,DGIK,DOID,DVLA,ELSA,EMTK,ERAA,EXCL,FPNI,FREN,HRUM,ICBP,INAF,INCO,INDR,INDY,INKP,INTA,JECC,JRPT,JSMR,KAEF,KBLI,KLBF,KOIN,KPIG,LMPI,MEDC,MERK,META,MLBI,MLIA,MTDL,MYOH,PANR,PGAS,PICO,PJAA,PNBN,PNIN,PTPP,PTSN,PWON,PYFA,RICY,SAFE,SDMU,SHID,SMDM,SMDR,SMGR,SRAJ,STTP,TBLA,TLKM,TMAS,TOWR,TSPC,UNTR,UNVR,WEHA,WINS,YPAS,ZBRA)
all_stocks <- do.call(data.frame,
lapply(all_stocks,function(x) replace(x, is.infinite(x), NA)))
cor(all_stocks$dret, all_stocks$Liquid, use="complete.obs")## [1] 0.01487997Hasil korelasinya adalah 0.01487997. Ini menunjukkan bahwa Daily Return dan Liquidity menunjukkan kecenderungan untuk bergerak bersama-sama. Oleh karena itu, kita bisa menolak H0. Namun, nilai korelasi tersebut bisa dikatakan sangat lemah karena mendekati angka 0. Ini bisa mengindikasikan bahwa strategi investasi yang mencoba memanfaatkan hubungan antara perubahan harga saham dan juga jumlah transaksi saham kurang bisa diandalkan ketika berinvestasi di pasar saham Indonesia, dan lebih baik untuk mencari faktor-faktor lain yang kemungkinan bisa lebih menjelaskan pergerakan harga saham.
Dengan melakukan analisa korelasi terhadap 113 saham yang terdaftar di BEI, penulis menemukan bahwa terdapat korelasi positif lemah antara pergerakan harga saham (Daily Return) dengan jumlah transaksi saham (Liquidity) yang terjadi setiap harinya. Hubungan yang lemah tersebut, meskipun membuktikan tetap ada korelasi diantara keduanya, mengimplikasikan bahwa jumlah transaksi saham kurang membantu dalam memamksimalkan keuntungan dari berinvestasi saham di Indonesia.
Bagi yang ingin melakukan penelitian yang serupa, penulis menyarankan untuk mencari variable-variable lain yang menunjukkan hubungan yang kuat dengan pergerakan saham. Selain itu, penulis juga menyarankan agar analisa yang dilakukan tidak hanya sampai pada analisa korelasi, namun juga melakukan analisa regresi jika memungkinkan.
Sebagai penutup, penulis meminta maaf apabila terdapat salah kata dalam artikel ini, dan mengucapkan terima kasih untuk telah membaca artikel ini.
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