# Q1. Install library tidyquant, timetk and quantmod. Import data tej_2016_2018.txt.
library(timetk)
library(tidyquant)
## Loading required package: lubridate
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
## Loading required package: PerformanceAnalytics
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
## Loading required package: quantmod
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
## ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
library(quantmod)
library(tidyr)
library(ggplot2)
library(readr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:xts':
##
## first, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
rm(list = ls())
tej_stock <- read_tsv("tej_2016_2018.txt")
## Rows: 605034 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): CoName, MV%
## dbl (10): CO_ID, Date, Open, High, Low, Close, Volume, Amount, Shares, Marke...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(tej_stock)
## Rows: 605,034
## Columns: 12
## $ CO_ID <dbl> 1101, 1102, 1103, 1104, 1108, 1109, 1110, 1201, 1203, 12…
## $ CoName <chr> "Taiwan Cement", "Asia Cement", "Chia Hsin Cement", "Uni…
## $ Date <dbl> 20160104, 20160104, 20160104, 20160104, 20160104, 201601…
## $ Open <dbl> 16.1059, 20.3594, 7.4338, 15.6636, 8.0961, 8.4734, 14.78…
## $ High <dbl> 16.1059, 20.3594, 7.4338, 15.6636, 8.0961, 8.4734, 14.78…
## $ Low <dbl> 15.4274, 19.2801, 7.3169, 15.2755, 8.0160, 8.3895, 14.35…
## $ Close <dbl> 15.4274, 19.5406, 7.3247, 15.2755, 8.0160, 8.3895, 14.54…
## $ Volume <dbl> 10437, 6852, 133, 231, 81, 15, 37, 1417, 9, 1504, 2, 395…
## $ Amount <dbl> 275489, 179618, 1254, 5076, 813, 156, 563, 24270, 215, 3…
## $ Shares <dbl> 3692175, 3361447, 774780, 628289, 404738, 378900, 572000…
## $ `Market Cap.` <dbl> 96550, 88237, 7282, 13602, 4047, 3789, 8694, 8729, 5496,…
## $ `MV%` <chr> "0.406", "0.371", "0.031", "0.057", "0.017", "0.016", "0…
# Q2. Select column CO_ID, DATE, Close. Convert CO_ID into text format, DATE into date format and change data from long format to wide format. (Hint: You can use `spread()`)
tej_stock$CO_ID <- as.character(tej_stock$CO_ID)
tej_stock$Date <- as.character(tej_stock$Date)
tej_stock$Date <- as.Date(tej_stock$Date, "%Y%m%d")
tej_stock %>%
select(CO_ID, Date, Close) %>%
spread(key = CO_ID, value = Close)
## # A tibble: 737 × 850
## Date `1101` `1102` `1103` `1104` `1108` `1109` `1110` `1201` `1203`
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016-01-04 15.4 19.5 7.32 15.3 8.02 8.39 14.5 15.4 18.0
## 2 2016-01-05 15.6 19.4 7.29 15.2 8.01 8.39 14.5 15.3 18.0
## 3 2016-01-06 15.4 19.3 7.22 15.1 7.98 8.38 14.8 15.2 18.0
## 4 2016-01-07 16.1 19.5 7.22 15.1 7.86 8.39 14.5 15.2 17.8
## 5 2016-01-08 15.9 19.4 7.19 15.1 7.92 8.35 14.2 15.3 18.0
## 6 2016-01-11 15.3 19.1 7.09 15.0 7.87 8.31 13.5 15.1 18.0
## 7 2016-01-12 15.4 19.0 7.04 14.7 7.86 8.31 13.6 15.0 17.9
## 8 2016-01-13 15.8 19.2 7.04 14.9 7.79 8.31 13.4 15.1 17.8
## 9 2016-01-14 15.8 19.0 7.06 14.5 7.66 8.31 13.2 15.2 17.8
## 10 2016-01-15 15.9 19.0 7.07 14.5 7.59 8.32 13.4 15.1 17.7
## # … with 727 more rows, and 840 more variables: 1210 <dbl>, 1213 <dbl>,
## # 1215 <dbl>, 1216 <dbl>, 1217 <dbl>, 1218 <dbl>, 1219 <dbl>, 1220 <dbl>,
## # 1225 <dbl>, 1227 <dbl>, 1229 <dbl>, 1231 <dbl>, 1232 <dbl>, 1233 <dbl>,
## # 1234 <dbl>, 1235 <dbl>, 1236 <dbl>, 1301 <dbl>, 1303 <dbl>, 1304 <dbl>,
## # 1305 <dbl>, 1307 <dbl>, 1308 <dbl>, 1309 <dbl>, 1310 <dbl>, 1312 <dbl>,
## # 1313 <dbl>, 1314 <dbl>, 1315 <dbl>, 1316 <dbl>, 1319 <dbl>, 1321 <dbl>,
## # 1323 <dbl>, 1324 <dbl>, 1325 <dbl>, 1326 <dbl>, 1339 <dbl>, 1402 <dbl>, …
#Q3. Convert data into xts format (Hint: you can use`tk_xts()`). Compute daily returns (you can use log or `Return.calculate()`). Show the first five daily returns.
ret_day <- tej_stock %>%
tk_xts(select = -Date, date_var = Date) %>%
Return.calculate(method = "log")
## Warning: Non-numeric columns being dropped: CO_ID, CoName, MV%
ret_day[is.na(ret_day)] <- 0
head(ret_day)
## Open High Low Close Volume
## 2016-01-04 0.00000000 0.00000000 0.00000000 0.00000000 0.0000000
## 2016-01-04 0.23435706 0.23435706 0.22292833 0.23634920 -0.4208166
## 2016-01-04 -1.00750555 -1.00750555 -0.96888673 -0.98124215 -3.9419467
## 2016-01-04 0.74530238 0.74530238 0.73606350 0.73499804 0.5520686
## 2016-01-04 -0.65995708 -0.65995708 -0.64481069 -0.64481069 -1.0479686
## 2016-01-04 0.04554938 0.04554938 0.04554138 0.04554138 -1.6863990
## Amount Shares Market Cap.
## 2016-01-04 0.0000000 0.00000000 0.00000000
## 2016-01-04 -0.4277153 -0.09384418 -0.09003463
## 2016-01-04 -4.9644939 -1.46754770 -2.49462083
## 2016-01-04 1.3981851 -0.20957887 0.62481129
## 2016-01-04 -1.8315477 -0.43976031 -1.21224097
## 2016-01-04 -1.6508751 -0.06596763 -0.06587373
#Q4. Select the first 10 stocks and compute monthly returns. (Hint: you can use `to.monthly()` or `to.period()` to convert daily data into monthly data. Show the first 10 monthly returns.
ret_mon <- tej_stock %>%
tk_xts(select = -Date, date_var = Date) %>%
to.period(period = "months",
indexAt = "lastof",
OHLC = FALSE) %>%
Return.calculate(method = 'log')
## Warning: Non-numeric columns being dropped: CO_ID, CoName, MV%
ret_mon[is.na(ret_mon)] <- 0
ret_mon[1:10,]
## Open High Low Close Volume
## 2016-01-31 0.000000000 0.000000000 0.000000000 0.000000000 0.0000000
## 2016-02-29 0.080908333 0.105868001 0.099580194 0.094358498 1.8640801
## 2016-03-31 0.000000000 -0.018604401 -0.009523469 -0.004750398 -1.3785723
## 2016-04-30 -0.014319432 -0.023753060 -0.014457450 -0.019230521 0.9132091
## 2016-05-31 -0.034231650 -0.034231650 -0.036581773 -0.029557478 -0.5337194
## 2016-06-30 -0.013019121 -0.009999629 -0.009104348 -0.008031805 -1.2580400
## 2016-07-31 0.009076666 0.019485946 0.016128643 0.010109097 0.4883528
## 2016-08-31 0.038774859 0.026350180 0.032784691 0.030707399 0.6346510
## 2016-09-30 0.001004093 0.000000000 -0.002014410 0.004027639 -0.7552790
## 2016-10-31 0.014887914 0.019798136 0.024941445 0.023809618 -0.4446858
## Amount Shares Market Cap.
## 2016-01-31 0.0000000 0 0.000000000
## 2016-02-29 1.9572593 0 0.094237684
## 2016-03-31 -1.3801249 0 -0.004609483
## 2016-04-30 0.8872181 0 -0.019176540
## 2016-05-31 -0.5699491 0 -0.029741969
## 2016-06-30 -1.2569868 0 -0.008119124
## 2016-07-31 0.4653632 0 -0.030347156
## 2016-08-31 0.6588446 0 0.030347156
## 2016-09-30 -0.7412156 0 0.004338402
## 2016-10-31 -0.4130697 0 0.023530497
# Q5. Find the 20 largest market capitalization firms at the end of 2017 and 2018. And compute each firm’s market capitalization share of the total 20 largest firms. (Hint: `filter()`, `arrange()`, `slice()`, `sum()`)。
largest20_2017 <- tej_stock %>%
select(CO_ID, CoName, Date, Shares) %>%
filter(Date == "2017-12-29") %>%
mutate(year1 = year(Date)) %>%
select(Date, year1, Shares, CO_ID, CoName) %>%
arrange(desc(Shares)) %>%
slice(1:20) %>%
ungroup()
glimpse(largest20_2017)
## Rows: 20
## Columns: 5
## $ Date <date> 2017-12-29, 2017-12-29, 2017-12-29, 2017-12-29, 2017-12-29, 20…
## $ year1 <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 201…
## $ Shares <dbl> 25930380, 19496989, 17328738, 15734860, 14976819, 13599823, 126…
## $ CO_ID <chr> "2330", "2891", "2317", "2002", "2883", "2886", "2303", "2882",…
## $ CoName <chr> "TSMC", "CTBC Holding", "Hon Hai Precision", "China Steel", "Ch…
largest20_2018 <- tej_stock %>%
select(CO_ID, CoName, Date, Shares) %>%
filter(Date == "2018-12-28") %>%
mutate(year2 = year(Date)) %>%
select(Date, year2, Shares, CO_ID, CoName) %>%
arrange(desc(Shares)) %>%
slice(1:20) %>%
ungroup()
glimpse(largest20_2018)
## Rows: 20
## Columns: 5
## $ Date <date> 2018-12-28, 2018-12-28, 2018-12-28, 2018-12-28, 2018-12-28, 20…
## $ year2 <dbl> 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 201…
## $ Shares <dbl> 25930380, 19496990, 15734861, 14963356, 13862991, 13599824, 125…
## $ CO_ID <chr> "2330", "2891", "2002", "2883", "2317", "2886", "5880", "2882",…
## $ CoName <chr> "TSMC", "CTBC Holding", "China Steel", "China Deve. FHC", "Hon …
#Q6. Plot the share of each firm’s market cap in Q5 in descending order.
largest20_2017%>%
ggplot(aes(x = reorder(CO_ID, Shares, desc), y = Shares)) +
geom_col(fill = "black") +
labs(x = "CO_ID") +
labs(y = 'Market share %')+
labs(title = 'The share of each firm’s market cap in Q5 in descending order') +
theme(axis.text.x = element_text(face="bold", color="#001000", size=8, angle=90), axis.text.y = element_text(face="bold", color="#001000", size=8, angle=0))
largest20_2018%>%
ggplot(aes(x = reorder(CO_ID, Shares, desc), y = Shares)) +
geom_col(fill = "grey") +
labs(x = "CO_ID") +
labs(y = 'Market share %')+
labs(title = 'The share of each firm’s market cap in Q5 in descending order') +
theme(axis.text.x = element_text(face="bold", color="#001000", size=8, angle=90), axis.text.y = element_text(face="bold", color="#001000", size=8, angle=0))
#Q7. Based on the 20 firms selected from Q5, find their daily returns in 2018. (Hint: `filter()`)
ret_day_2018 <- largest20_2018 %>%
tk_xts(select = -Date, date_var = Date) %>%
Return.calculate(method = "log")
## Warning: Non-numeric columns being dropped: CO_ID, CoName
ret_day_2018[is.na(ret_day_2018)] <- 0
head(ret_day_2018)
## year2 Shares
## 2018-12-28 0 0.00000000
## 2018-12-28 0 -0.28515516
## 2018-12-28 0 -0.21438140
## 2018-12-28 0 -0.05027442
## 2018-12-28 0 -0.07638151
## 2018-12-28 0 -0.01916592