setwd("C:/Users/Aishwarya/Desktop/CUNY/Assignments - Week2")
# Question 1
acme = read.csv("acme.csv") # read csv file
acme
## X month market acme
## 1 1 1/86 -0.061134 0.030160
## 2 2 2/86 0.008220 -0.165457
## 3 3 3/86 -0.007381 0.080137
## 4 4 4/86 -0.067561 -0.109917
## 5 5 5/86 -0.006238 -0.114853
## 6 6 6/86 -0.044251 -0.099254
## 7 7 7/86 -0.112070 -0.226846
## 8 8 8/86 0.030226 0.073445
## 9 9 9/86 -0.129556 -0.143064
## 10 10 10/86 0.001319 0.034776
## 11 11 11/86 -0.033679 -0.063375
## 12 12 12/86 -0.072795 -0.058735
## 13 13 1/87 0.073396 0.050214
## 14 14 2/87 -0.011618 0.111165
## 15 15 3/87 -0.026852 -0.127492
## 16 16 4/87 -0.040356 0.054522
## 17 17 5/87 -0.047539 -0.072918
## 18 18 6/87 -0.001732 -0.058979
## 19 19 7/87 -0.008899 0.236147
## 20 20 8/87 -0.020837 -0.094778
## 21 21 9/87 -0.084811 -0.135669
## 22 22 10/87 -0.262077 -0.284796
## 23 23 11/87 -0.110167 -0.171494
## 24 24 12/87 0.034955 0.242616
## 25 25 1/88 0.012688 -0.063518
## 26 26 2/88 -0.002170 -0.117677
## 27 27 3/88 -0.073462 0.201674
## 28 28 4/88 -0.043419 -0.147728
## 29 29 5/88 -0.054730 -0.170885
## 30 30 6/88 -0.011755 -0.014893
## 31 31 7/88 -0.061718 -0.110515
## 32 32 8/88 -0.101710 -0.168769
## 33 33 9/88 -0.032705 -0.135585
## 34 34 10/88 -0.045334 -0.084077
## 35 35 11/88 -0.079288 -0.164550
## 36 36 12/88 -0.036233 0.150269
## 37 37 1/89 -0.011494 -0.015672
## 38 38 2/89 -0.093729 -0.037860
## 39 39 3/89 -0.065215 -0.074712
## 40 40 4/89 -0.037113 -0.108530
## 41 41 5/89 -0.044399 -0.036769
## 42 42 6/89 -0.084412 0.023912
## 43 43 7/89 0.003444 -0.078430
## 44 44 8/89 -0.056760 -0.132199
## 45 45 9/89 -0.078970 -0.110141
## 46 46 10/89 -0.105367 -0.126302
## 47 47 11/89 -0.038634 -0.095730
## 48 48 12/89 -0.043261 0.065740
## 49 49 1/90 -0.139773 -0.120056
## 50 50 2/90 -0.059094 -0.085205
## 51 51 3/90 -0.057736 -0.130433
## 52 52 4/90 -0.102524 -0.116728
## 53 53 5/90 0.023881 -0.078039
## 54 54 6/90 -0.079116 -0.170322
## 55 55 7/90 -0.078965 -0.077727
## 56 56 8/90 -0.161359 -0.277035
## 57 57 9/90 -0.119376 -0.207595
## 58 58 10/90 -0.076008 -0.070515
## 59 59 11/90 -0.006444 -0.046274
## 60 60 12/90 -0.026401 -0.190834
summary(acme)
## X month market acme
## Min. : 1.00 1/86 : 1 Min. :-0.26208 Min. :-0.28480
## 1st Qu.:15.75 1/87 : 1 1st Qu.:-0.07901 1st Qu.:-0.13305
## Median :30.50 1/88 : 1 Median :-0.04487 Median :-0.08999
## Mean :30.50 1/89 : 1 Mean :-0.05117 Mean :-0.06897
## 3rd Qu.:45.25 1/90 : 1 3rd Qu.:-0.01159 3rd Qu.:-0.03149
## Max. :60.00 10/86 : 1 Max. : 0.07340 Max. : 0.24262
## (Other):54
mean(acme$market)
## [1] -0.0511683
mean(acme$acme)
## [1] -0.06896925
quantile(acme$market,probs = c(0.5))
## 50%
## -0.0448665
quantile(acme$acme,probs = c(0.5))
## 50%
## -0.0899915
#Question 2
# select variables v1, v3, v4
myvars <- c("X", "market", "acme")
acmex <- acme[myvars]
newacme<-acmex[1:40,]
newacme
## X market acme
## 1 1 -0.061134 0.030160
## 2 2 0.008220 -0.165457
## 3 3 -0.007381 0.080137
## 4 4 -0.067561 -0.109917
## 5 5 -0.006238 -0.114853
## 6 6 -0.044251 -0.099254
## 7 7 -0.112070 -0.226846
## 8 8 0.030226 0.073445
## 9 9 -0.129556 -0.143064
## 10 10 0.001319 0.034776
## 11 11 -0.033679 -0.063375
## 12 12 -0.072795 -0.058735
## 13 13 0.073396 0.050214
## 14 14 -0.011618 0.111165
## 15 15 -0.026852 -0.127492
## 16 16 -0.040356 0.054522
## 17 17 -0.047539 -0.072918
## 18 18 -0.001732 -0.058979
## 19 19 -0.008899 0.236147
## 20 20 -0.020837 -0.094778
## 21 21 -0.084811 -0.135669
## 22 22 -0.262077 -0.284796
## 23 23 -0.110167 -0.171494
## 24 24 0.034955 0.242616
## 25 25 0.012688 -0.063518
## 26 26 -0.002170 -0.117677
## 27 27 -0.073462 0.201674
## 28 28 -0.043419 -0.147728
## 29 29 -0.054730 -0.170885
## 30 30 -0.011755 -0.014893
## 31 31 -0.061718 -0.110515
## 32 32 -0.101710 -0.168769
## 33 33 -0.032705 -0.135585
## 34 34 -0.045334 -0.084077
## 35 35 -0.079288 -0.164550
## 36 36 -0.036233 0.150269
## 37 37 -0.011494 -0.015672
## 38 38 -0.093729 -0.037860
## 39 39 -0.065215 -0.074712
## 40 40 -0.037113 -0.108530
#Question 3
colnames(newacme) <- c("X","mkt_alpha", "acme_alpha")
newacme
## X mkt_alpha acme_alpha
## 1 1 -0.061134 0.030160
## 2 2 0.008220 -0.165457
## 3 3 -0.007381 0.080137
## 4 4 -0.067561 -0.109917
## 5 5 -0.006238 -0.114853
## 6 6 -0.044251 -0.099254
## 7 7 -0.112070 -0.226846
## 8 8 0.030226 0.073445
## 9 9 -0.129556 -0.143064
## 10 10 0.001319 0.034776
## 11 11 -0.033679 -0.063375
## 12 12 -0.072795 -0.058735
## 13 13 0.073396 0.050214
## 14 14 -0.011618 0.111165
## 15 15 -0.026852 -0.127492
## 16 16 -0.040356 0.054522
## 17 17 -0.047539 -0.072918
## 18 18 -0.001732 -0.058979
## 19 19 -0.008899 0.236147
## 20 20 -0.020837 -0.094778
## 21 21 -0.084811 -0.135669
## 22 22 -0.262077 -0.284796
## 23 23 -0.110167 -0.171494
## 24 24 0.034955 0.242616
## 25 25 0.012688 -0.063518
## 26 26 -0.002170 -0.117677
## 27 27 -0.073462 0.201674
## 28 28 -0.043419 -0.147728
## 29 29 -0.054730 -0.170885
## 30 30 -0.011755 -0.014893
## 31 31 -0.061718 -0.110515
## 32 32 -0.101710 -0.168769
## 33 33 -0.032705 -0.135585
## 34 34 -0.045334 -0.084077
## 35 35 -0.079288 -0.164550
## 36 36 -0.036233 0.150269
## 37 37 -0.011494 -0.015672
## 38 38 -0.093729 -0.037860
## 39 39 -0.065215 -0.074712
## 40 40 -0.037113 -0.108530
#Question 4
summary(newacme)
## X mkt_alpha acme_alpha
## Min. : 1.00 Min. :-0.262077 Min. :-0.28480
## 1st Qu.:10.75 1st Qu.:-0.068870 1st Qu.:-0.13561
## Median :20.50 Median :-0.038734 Median :-0.07939
## Mean :20.50 Mean :-0.043471 Mean :-0.05194
## 3rd Qu.:30.25 3rd Qu.:-0.008519 3rd Qu.: 0.03131
## Max. :40.00 Max. : 0.073396 Max. : 0.24262
mean(newacme$mkt_alpha)
## [1] -0.0434706
mean(newacme$acme_alpha)
## [1] -0.05193682
quantile(newacme$mkt_alpha,probs = c(0.5))
## 50%
## -0.0387345
quantile(newacme$acme_alpha,probs = c(0.5))
## 50%
## -0.0793945
#question 5
require(stringi)
## Loading required package: stringi
acme$month <- as.character(acme$month)
for (i in 1:60) {
if(stri_detect_fixed(acme$month[i],"86")==TRUE)
{
acme$month[i]<-"1986"
}
}
#question 6
acme
## X month market acme
## 1 1 1986 -0.061134 0.030160
## 2 2 1986 0.008220 -0.165457
## 3 3 1986 -0.007381 0.080137
## 4 4 1986 -0.067561 -0.109917
## 5 5 1986 -0.006238 -0.114853
## 6 6 1986 -0.044251 -0.099254
## 7 7 1986 -0.112070 -0.226846
## 8 8 1986 0.030226 0.073445
## 9 9 1986 -0.129556 -0.143064
## 10 10 1986 0.001319 0.034776
## 11 11 1986 -0.033679 -0.063375
## 12 12 1986 -0.072795 -0.058735
## 13 13 1/87 0.073396 0.050214
## 14 14 2/87 -0.011618 0.111165
## 15 15 3/87 -0.026852 -0.127492
## 16 16 4/87 -0.040356 0.054522
## 17 17 5/87 -0.047539 -0.072918
## 18 18 6/87 -0.001732 -0.058979
## 19 19 7/87 -0.008899 0.236147
## 20 20 8/87 -0.020837 -0.094778
## 21 21 9/87 -0.084811 -0.135669
## 22 22 10/87 -0.262077 -0.284796
## 23 23 11/87 -0.110167 -0.171494
## 24 24 12/87 0.034955 0.242616
## 25 25 1/88 0.012688 -0.063518
## 26 26 2/88 -0.002170 -0.117677
## 27 27 3/88 -0.073462 0.201674
## 28 28 4/88 -0.043419 -0.147728
## 29 29 5/88 -0.054730 -0.170885
## 30 30 6/88 -0.011755 -0.014893
## 31 31 7/88 -0.061718 -0.110515
## 32 32 8/88 -0.101710 -0.168769
## 33 33 9/88 -0.032705 -0.135585
## 34 34 10/88 -0.045334 -0.084077
## 35 35 11/88 -0.079288 -0.164550
## 36 36 12/88 -0.036233 0.150269
## 37 37 1/89 -0.011494 -0.015672
## 38 38 2/89 -0.093729 -0.037860
## 39 39 3/89 -0.065215 -0.074712
## 40 40 4/89 -0.037113 -0.108530
## 41 41 5/89 -0.044399 -0.036769
## 42 42 6/89 -0.084412 0.023912
## 43 43 7/89 0.003444 -0.078430
## 44 44 8/89 -0.056760 -0.132199
## 45 45 9/89 -0.078970 -0.110141
## 46 46 10/89 -0.105367 -0.126302
## 47 47 11/89 -0.038634 -0.095730
## 48 48 12/89 -0.043261 0.065740
## 49 49 1/90 -0.139773 -0.120056
## 50 50 2/90 -0.059094 -0.085205
## 51 51 3/90 -0.057736 -0.130433
## 52 52 4/90 -0.102524 -0.116728
## 53 53 5/90 0.023881 -0.078039
## 54 54 6/90 -0.079116 -0.170322
## 55 55 7/90 -0.078965 -0.077727
## 56 56 8/90 -0.161359 -0.277035
## 57 57 9/90 -0.119376 -0.207595
## 58 58 10/90 -0.076008 -0.070515
## 59 59 11/90 -0.006444 -0.046274
## 60 60 12/90 -0.026401 -0.190834
#question 7
data_market <- read.table(file="https://raw.githubusercontent.com/chitrarth2018/Assignment_CUNY/master/acme.csv", header=TRUE, sep=",")
data_market
## X month market acme
## 1 1 1/86 -0.061134 0.030160
## 2 2 2/86 0.008220 -0.165457
## 3 3 3/86 -0.007381 0.080137
## 4 4 4/86 -0.067561 -0.109917
## 5 5 5/86 -0.006238 -0.114853
## 6 6 6/86 -0.044251 -0.099254
## 7 7 7/86 -0.112070 -0.226846
## 8 8 8/86 0.030226 0.073445
## 9 9 9/86 -0.129556 -0.143064
## 10 10 10/86 0.001319 0.034776
## 11 11 11/86 -0.033679 -0.063375
## 12 12 12/86 -0.072795 -0.058735
## 13 13 1/87 0.073396 0.050214
## 14 14 2/87 -0.011618 0.111165
## 15 15 3/87 -0.026852 -0.127492
## 16 16 4/87 -0.040356 0.054522
## 17 17 5/87 -0.047539 -0.072918
## 18 18 6/87 -0.001732 -0.058979
## 19 19 7/87 -0.008899 0.236147
## 20 20 8/87 -0.020837 -0.094778
## 21 21 9/87 -0.084811 -0.135669
## 22 22 10/87 -0.262077 -0.284796
## 23 23 11/87 -0.110167 -0.171494
## 24 24 12/87 0.034955 0.242616
## 25 25 1/88 0.012688 -0.063518
## 26 26 2/88 -0.002170 -0.117677
## 27 27 3/88 -0.073462 0.201674
## 28 28 4/88 -0.043419 -0.147728
## 29 29 5/88 -0.054730 -0.170885
## 30 30 6/88 -0.011755 -0.014893
## 31 31 7/88 -0.061718 -0.110515
## 32 32 8/88 -0.101710 -0.168769
## 33 33 9/88 -0.032705 -0.135585
## 34 34 10/88 -0.045334 -0.084077
## 35 35 11/88 -0.079288 -0.164550
## 36 36 12/88 -0.036233 0.150269
## 37 37 1/89 -0.011494 -0.015672
## 38 38 2/89 -0.093729 -0.037860
## 39 39 3/89 -0.065215 -0.074712
## 40 40 4/89 -0.037113 -0.108530
## 41 41 5/89 -0.044399 -0.036769
## 42 42 6/89 -0.084412 0.023912
## 43 43 7/89 0.003444 -0.078430
## 44 44 8/89 -0.056760 -0.132199
## 45 45 9/89 -0.078970 -0.110141
## 46 46 10/89 -0.105367 -0.126302
## 47 47 11/89 -0.038634 -0.095730
## 48 48 12/89 -0.043261 0.065740
## 49 49 1/90 -0.139773 -0.120056
## 50 50 2/90 -0.059094 -0.085205
## 51 51 3/90 -0.057736 -0.130433
## 52 52 4/90 -0.102524 -0.116728
## 53 53 5/90 0.023881 -0.078039
## 54 54 6/90 -0.079116 -0.170322
## 55 55 7/90 -0.078965 -0.077727
## 56 56 8/90 -0.161359 -0.277035
## 57 57 9/90 -0.119376 -0.207595
## 58 58 10/90 -0.076008 -0.070515
## 59 59 11/90 -0.006444 -0.046274
## 60 60 12/90 -0.026401 -0.190834