Assignment 5- session 5
sol:
> z<-read.csv(file.choose(),header=T)
> head(z)
Date Open High Low Close Shares.Traded Turnover..Rs..Cr.
1 02-Jul-2012 5283.85 5302.15 5263.35 5278.60 126161441 4991.57
2 03-Jul-2012 5298.85 5317. 133117055 5161.82
3 04-Jul-2012 5310.40 5317.65 5273.30 5302.55 155995887 5750.10
4 05-Jul-2012 5297.05 5333.65 5288.85 5327.30 118915392 4709.79
5 06-Jul-2012 5324.70 5327.20 5287.75 5316.95 113300726 4760.51
6 09-Jul-2012 5283.70 5300.60 5257.75 5275.15 101169926 4189.25
> open<-z$Open[10:95]
> open.ts<-ts(open,deltat=1/252)
> open.ts
Time Series:
Start = c(1, 1)
End = c(1, 86)
Frequency = 252
[1] 5242.75 5232.35 5228.05 5199.10 5249.85 5233.55 5163.25 5128.80 5118.40
[10] 5126.30 5124.30 5129.75 5214.85 5220.70 5233.10 5195.60 5260.85 5295.40
[19] 5345.25 5348.30 5308.20 5316.35 5343.25 5385.95 5368.60 5368.70 5395.75
[28] 5426.15 5392.60 5387.85 5348.05 5343.85 5268.60 5298.20 5276.50 5249.15
[37] 5243.90 5217.65 5309.45 5343.65 5361.90 5336.10 5404.45 5435.20 5528.35
[46] 5631.75 5602.40 5536.95 5577.00 5691.95 5674.90 5653.40 5673.75 5684.80
[55] 5704.75 5727.70 5751.55 5815.00 5751.85 5708.15 5671.15 5663.50 5681.70
[64] 5674.25 5705.60 5681.10 5675.30 5703.30 5667.60 5715.65 5688.80 5683.55
[73] 5665.20 5656.35 5596.75 5609.85 5696.35 5693.05 5694.10 5718.60 5709.00
[82] 5731.10 5688.45 5689.70 5650.35 5624.80
> summary(open.ts)
Min. 1st Qu. Median Mean 3rd Qu. Max.
5118 5281 5431 5474 5682 5815
> z.diff<-diff(open.ts)
> z.diff
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
[1] -10.40 -4.30 -28.95 50.75 -16.30 -70.30 -34.45 -10.40 7.90 -2.00
[11] 5.45 85.10 5.85 12.40 -37.50 65.25 34.55 49.85 3.05 -40.10
[21] 8.15 26.90 42.70 -17.35 0.10 27.05 30.40 -33.55 -4.75 -39.80
[31] -4.20 -75.25 29.60 -21.70 -27.35 -5.25 -26.25 91.80 34.20 18.25
[41] -25.80 68.35 30.75 93.15 103.40 -29.35 -65.45 40.05 114.95 -17.05
[51] -21.50 20.35 11.05 19.95 22.95 23.85 63.45 -63.15 -43.70 -37.00
[61] -7.65 18.20 -7.45 31.35 -24.50 -5.80 28.00 -35.70 48.05 -26.85
[71] -5.25 -18.35 -8.85 -59.60 13.10 86.50 -3.30 1.05 24.50 -9.60
[81] 22.10 -42.65 1.25 -39.35 -25.55
> returns<-cbind(open.ts,z.diff,lag(open.ts,k=-1))
> returns
Time Series:
Start = c(1, 1)
End = c(1, 87)
Frequency = 252
open.ts z.diff lag(open.ts, k = -1)
1.000000 5242.75 NA NA
1.003968 5232.35 -10.40 5242.75
1.007937 5228.05 -4.30 5232.35
1.011905 5199.10 -28.95 5228.05
1.015873 5249.85 50.75 5199.10
1.019841 5233.55 -16.30 5249.85
1.023810 5163.25 -70.30 5233.55
1.027778 5128.80 -34.45 5163.25
1.031746 5118.40 -10.40 5128.80
1.035714 5126.30 7.90 5118.40
1.039683 5124.30 -2.00 5126.30
1.043651 5129.75 5.45 5124.30
1.047619 5214.85 85.10 5129.75
1.051587 5220.70 5.85 5214.85
1.055556 5233.10 12.40 5220.70
1.059524 5195.60 -37.50 5233.10
1.063492 5260.85 65.25 5195.60
1.067460 5295.40 34.55 5260.85
1.071429 5345.25 49.85 5295.40
1.075397 5348.30 3.05 5345.25
1.079365 5308.20 -40.10 5348.30
1.083333 5316.35 8.15 5308.20
1.087302 5343.25 26.90 5316.35
1.091270 5385.95 42.70 5343.25
1.095238 5368.60 -17.35 5385.95
1.099206 5368.70 0.10 5368.60
1.103175 5395.75 27.05 5368.70
1.107143 5426.15 30.40 5395.75
1.111111 5392.60 -33.55 5426.15
1.115079 5387.85 -4.75 5392.60
1.119048 5348.05 -39.80 5387.85
1.123016 5343.85 -4.20 5348.05
1.126984 5268.60 -75.25 5343.85
1.130952 5298.20 29.60 5268.60
1.134921 5276.50 -21.70 5298.20
1.138889 5249.15 -27.35 5276.50
1.142857 5243.90 -5.25 5249.15
1.146825 5217.65 -26.25 5243.90
1.150794 5309.45 91.80 5217.65
1.154762 5343.65 34.20 5309.45
1.158730 5361.90 18.25 5343.65
1.162698 5336.10 -25.80 5361.90
1.166667 5404.45 68.35 5336.10
1.170635 5435.20 30.75 5404.45
1.174603 5528.35 93.15 5435.20
1.178571 5631.75 103.40 5528.35
1.182540 5602.40 -29.35 5631.75
1.186508 5536.95 -65.45 5602.40
1.190476 5577.00 40.05 5536.95
1.194444 5691.95 114.95 5577.00
1.198413 5674.90 -17.05 5691.95
1.202381 5653.40 -21.50 5674.90
1.206349 5673.75 20.35 5653.40
1.210317 5684.80 11.05 5673.75
1.214286 5704.75 19.95 5684.80
1.218254 5727.70 22.95 5704.75
1.222222 5751.55 23.85 5727.70
1.226190 5815.00 63.45 5751.55
1.230159 5751.85 -63.15 5815.00
1.234127 5708.15 -43.70 5751.85
1.238095 5671.15 -37.00 5708.15
1.242063 5663.50 -7.65 5671.15
1.246032 5681.70 18.20 5663.50
1.250000 5674.25 -7.45 5681.70
1.253968 5705.60 31.35 5674.25
1.257937 5681.10 -24.50 5705.60
1.261905 5675.30 -5.80 5681.10
1.265873 5703.30 28.00 5675.30
1.269841 5667.60 -35.70 5703.30
1.273810 5715.65 48.05 5667.60
1.277778 5688.80 -26.85 5715.65
1.281746 5683.55 -5.25 5688.80
1.285714 5665.20 -18.35 5683.55
1.289683 5656.35 -8.85 5665.20
1.293651 5596.75 -59.60 5656.35
1.297619 5609.85 13.10 5596.75
1.301587 5696.35 86.50 5609.85
1.305556 5693.05 -3.30 5696.35
1.309524 5694.10 1.05 5693.05
1.313492 5718.60 24.50 5694.10
1.317460 5709.00 -9.60 5718.60
1.321429 5731.10 22.10 5709.00
1.325397 5688.45 -42.65 5731.10
1.329365 5689.70 1.25 5688.45
1.333333 5650.35 -39.35 5689.70
1.337302 5624.80 -25.55 5650.35
1.341270 NA NA 5624.80
> plot(returns)
> returns<-z.diff/lag(open.ts,k=-1)
> returns
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
[1] -1.983692e-03 -8.218105e-04 -5.537437e-03 9.761305e-03 -3.104851e-03
[6] -1.343256e-02 -6.672154e-03 -2.027765e-03 1.543451e-03 -3.901449e-04
[11] 1.063560e-03 1.658950e-02 1.121796e-03 2.375160e-03 -7.165925e-03
[16] 1.255870e-02 6.567380e-03 9.413831e-03 5.706001e-04 -7.497710e-03
[21] 1.535360e-03 5.059862e-03 7.991391e-03 -3.221344e-03 1.862683e-05
[26] 5.038464e-03 5.634064e-03 -6.183021e-03 -8.808367e-04 -7.386991e-03
[31] -7.853330e-04 -1.408161e-02 5.618191e-03 -4.095731e-03 -5.183360e-03
[36] -1.000162e-03 -5.005816e-03 1.759413e-02 6.441345e-03 3.415269e-03
[41] -4.811727e-03 1.280898e-02 5.689756e-03 1.713828e-02 1.870359e-02
[46] -5.211524e-03 -1.168249e-02 7.233224e-03 2.061144e-02 -2.995458e-03
[51] -3.788613e-03 3.599604e-03 1.947566e-03 3.509358e-03 4.022963e-03
[56] 4.163975e-03 1.103181e-02 -1.085985e-02 -7.597556e-03 -6.481960e-03
[61] -1.348933e-03 3.213561e-03 -1.311227e-03 5.524959e-03 -4.294027e-03
[66] -1.020929e-03 4.933660e-03 -6.259534e-03 8.478015e-03 -4.697628e-03
[71] -9.228660e-04 -3.228616e-03 -1.562169e-03 -1.053683e-02 2.340644e-03
[76] 1.541931e-02 -5.793183e-04 1.844354e-04 4.302699e-03 -1.678733e-03
[81] 3.871081e-03 -7.441852e-03 2.197435e-04 -6.916006e-03 -4.521844e-03
> plot(returns)
Assignment 2: Do logit analysis for 700 data points and then predict for 150 data points.
sol:
z<-read.csv(file.choose(),header=T)
head(z)
z.data<-z[1:700,1:9]
sapply(z.data,mean)
z.data$ed<-factor(z.data$ed)
logit.est<-glm(default~age+employ+address+income+debtinc+creddebt+othdebt,data=z.data,family="binomial")
summary(logit.est)
confint.default(logit.est)
logit.eg2<-with(z[701:850,1:8],data.frame(age=mean(age),employ=mean(employ),address=mean(address),income=mean(income),debtinc=mean(debtinc),creddebt=mean(creddebt),othdebt=mean(othdebt),ed=factor(1:3)))
logit.eg2$prob<-predict(logit.est,newdata=logit.eg2,type="response")
head(logit.eg2)
sol:
z<-read.csv(file.choose(),header=T)
head(z)
z.data<-z[1:700,1:9]
sapply(z.data,mean)
z.data$ed<-factor(z.data$ed)
logit.est<-glm(default~age+employ+address+income+debtinc+creddebt+othdebt,data=z.data,family="binomial")
summary(logit.est)
confint.default(logit.est)
logit.eg2<-with(z[701:850,1:8],data.frame(age=mean(age),employ=mean(employ),address=mean(address),income=mean(income),debtinc=mean(debtinc),creddebt=mean(creddebt),othdebt=mean(othdebt),ed=factor(1:3)))
logit.eg2$prob<-predict(logit.est,newdata=logit.eg2,type="response")
head(logit.eg2)
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