Asgn1: To find and plot returns for NSE data of more than months.
>
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 5317.00 5265.95 5287.95 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
>
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)
Asgn 2: Do logit analysis for 700 data points and then predict for 150 data points.
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$ed)
logit.est<-glm(default~age+employ+address+income+debtinc+creddebt+othdebt,data=z.data,family="binomial")
summary(logit.est)
confint.default(logit.est)
logit.eg2<-with(z[701:850,1:8],data.frame(age=age,employ=employ,address=address,income=income,debtinc=debtinc,creddebt=creddebt,othdebt=othdebt,ed=factor(1:3)))
logit.eg2$prob<-predict(logit.est,newdata=logit.eg2,type="response")
head(logit.eg2)
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