IT Business Application Lab Assignments#5
Session 5
Date: 5th Feb,2013
Assignment 1
Find Returns of NSE data for greater than 6 months having selected the 10th data point as start and 95th data point as end.
Find plot of that return.
Data Set: S&P CNX NIFTY from 01/06/2012 to 31/01/2013
Output:
> z<-read.csv(file.choose(),header=T)
> head(z)
Date Open High Low Close Shares.Traded Turnover..Rs..Cr.
1 01-Jun-2012 4910.85 4925.00 4831.75 4841.60 138767416 4989.22
2 04-Jun-2012 4797.30 4858.30 4770.35 4848.15 152339865 5143.54
3 05-Jun-2012 4869.45 4898.95 4847.70 4863.30 141476962 5016.41
4 06-Jun-2012 4886.65 5010.45 4886.15 4997.10 185340406 7188.42
5 07-Jun-2012 5035.35 5059.65 5007.75 5049.65 150558164 6077.37
6 08-Jun-2012 5044.25 5084.45 4994.80 5068.35 138389395 5249.81
> 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] 5105.10 5069.55 5174.00 5050.80 5114.55 5097.35 5101.75 5158.50 5107.45 5149.45 5148.95 5191.25 5283.85 5298.85 5310.40 5297.05 5324.70 5283.70 5286.60 5315.25
[21] 5240.00 5242.75 5232.35 5228.05 5199.10 5249.85 5233.55 5163.25 5128.80 5118.40 5126.30 5124.30 5129.75 5214.85 5220.70 5233.10 5195.60 5260.85 5295.40 5345.25
[41] 5348.30 5308.20 5316.35 5343.25 5385.95 5368.60 5368.70 5395.75 5426.15 5392.60 5387.85 5348.05 5343.85 5268.60 5298.20 5276.50 5249.15 5243.90 5217.65 5309.45
[61] 5343.65 5361.90 5336.10 5404.45 5435.20 5528.35 5631.75 5602.40 5536.95 5577.00 5691.95 5674.90 5653.40 5673.75 5684.80 5704.75 5727.70 5751.55 5815.00 5751.85
[81] 5708.15 5671.15 5663.50 5681.70 5674.25 5705.60
> summary(open.ts)
Min. 1st Qu. Median Mean 3rd Qu. Max.
5051 5218 5309 5356 5433 5815
> z.diff<-diff(open.ts)
> z.diff
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
[1] -35.55 104.45 -123.20 63.75 -17.20 4.40 56.75 -51.05 42.00 -0.50 42.30 92.60 15.00 11.55 -13.35 27.65 -41.00 2.90 28.65 -75.25
[21] 2.75 -10.40 -4.30 -28.95 50.75 -16.30 -70.30 -34.45 -10.40 7.90 -2.00 5.45 85.10 5.85 12.40 -37.50 65.25 34.55 49.85 3.05
[41] -40.10 8.15 26.90 42.70 -17.35 0.10 27.05 30.40 -33.55 -4.75 -39.80 -4.20 -75.25 29.60 -21.70 -27.35 -5.25 -26.25 91.80 34.20
[61] 18.25 -25.80 68.35 30.75 93.15 103.40 -29.35 -65.45 40.05 114.95 -17.05 -21.50 20.35 11.05 19.95 22.95 23.85 63.45 -63.15 -43.70
[81] -37.00 -7.65 18.20 -7.45 31.35
> 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 5105.10 NA NA
1.003968 5069.55 -35.55 5105.10
1.007937 5174.00 104.45 5069.55
1.011905 5050.80 -123.20 5174.00
1.015873 5114.55 63.75 5050.80
1.019841 5097.35 -17.20 5114.55
1.023810 5101.75 4.40 5097.35
1.027778 5158.50 56.75 5101.75
1.031746 5107.45 -51.05 5158.50
1.035714 5149.45 42.00 5107.45
1.039683 5148.95 -0.50 5149.45
1.043651 5191.25 42.30 5148.95
1.047619 5283.85 92.60 5191.25
1.051587 5298.85 15.00 5283.85
1.055556 5310.40 11.55 5298.85
1.059524 5297.05 -13.35 5310.40
1.063492 5324.70 27.65 5297.05
1.067460 5283.70 -41.00 5324.70
1.071429 5286.60 2.90 5283.70
1.075397 5315.25 28.65 5286.60
1.079365 5240.00 -75.25 5315.25
1.083333 5242.75 2.75 5240.00
1.087302 5232.35 -10.40 5242.75
1.091270 5228.05 -4.30 5232.35
1.095238 5199.10 -28.95 5228.05
1.099206 5249.85 50.75 5199.10
1.103175 5233.55 -16.30 5249.85
1.107143 5163.25 -70.30 5233.55
1.111111 5128.80 -34.45 5163.25
1.115079 5118.40 -10.40 5128.80
1.119048 5126.30 7.90 5118.40
1.123016 5124.30 -2.00 5126.30
1.126984 5129.75 5.45 5124.30
1.130952 5214.85 85.10 5129.75
1.134921 5220.70 5.85 5214.85
1.138889 5233.10 12.40 5220.70
1.142857 5195.60 -37.50 5233.10
1.146825 5260.85 65.25 5195.60
1.150794 5295.40 34.55 5260.85
1.154762 5345.25 49.85 5295.40
1.158730 5348.30 3.05 5345.25
1.162698 5308.20 -40.10 5348.30
1.166667 5316.35 8.15 5308.20
1.170635 5343.25 26.90 5316.35
1.174603 5385.95 42.70 5343.25
1.178571 5368.60 -17.35 5385.95
1.182540 5368.70 0.10 5368.60
1.186508 5395.75 27.05 5368.70
1.190476 5426.15 30.40 5395.75
1.194444 5392.60 -33.55 5426.15
1.198413 5387.85 -4.75 5392.60
1.202381 5348.05 -39.80 5387.85
1.206349 5343.85 -4.20 5348.05
1.210317 5268.60 -75.25 5343.85
1.214286 5298.20 29.60 5268.60
1.218254 5276.50 -21.70 5298.20
1.222222 5249.15 -27.35 5276.50
1.226190 5243.90 -5.25 5249.15
1.230159 5217.65 -26.25 5243.90
1.234127 5309.45 91.80 5217.65
1.238095 5343.65 34.20 5309.45
1.242063 5361.90 18.25 5343.65
1.246032 5336.10 -25.80 5361.90
1.250000 5404.45 68.35 5336.10
1.253968 5435.20 30.75 5404.45
1.257937 5528.35 93.15 5435.20
1.261905 5631.75 103.40 5528.35
1.265873 5602.40 -29.35 5631.75
1.269841 5536.95 -65.45 5602.40
1.273810 5577.00 40.05 5536.95
1.277778 5691.95 114.95 5577.00
1.281746 5674.90 -17.05 5691.95
1.285714 5653.40 -21.50 5674.90
1.289683 5673.75 20.35 5653.40
1.293651 5684.80 11.05 5673.75
1.297619 5704.75 19.95 5684.80
1.301587 5727.70 22.95 5704.75
1.305556 5751.55 23.85 5727.70
1.309524 5815.00 63.45 5751.55
1.313492 5751.85 -63.15 5815.00
1.317460 5708.15 -43.70 5751.85
1.321429 5671.15 -37.00 5708.15
1.325397 5663.50 -7.65 5671.15
1.329365 5681.70 18.20 5663.50
1.333333 5674.25 -7.45 5681.70
1.337302 5705.60 31.35 5674.25
1.341270 NA NA 5705.60
> returns<-z.diff/lag(open.ts,k=-1)
> returns
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
[1] -6.963625e-03 2.060341e-02 -2.381136e-02 1.262176e-02 -3.362955e-03 8.631936e-04 1.112363e-02 -9.896288e-03 8.223282e-03 -9.709775e-05 8.215267e-03
[12] 1.783771e-02 2.838839e-03 2.179718e-03 -2.513935e-03 5.219887e-03 -7.699964e-03 5.488578e-04 5.419362e-03 -1.415738e-02 5.248092e-04 -1.983692e-03
[23] -8.218105e-04 -5.537437e-03 9.761305e-03 -3.104851e-03 -1.343256e-02 -6.672154e-03 -2.027765e-03 1.543451e-03 -3.901449e-04 1.063560e-03 1.658950e-02
[34] 1.121796e-03 2.375160e-03 -7.165925e-03 1.255870e-02 6.567380e-03 9.413831e-03 5.706001e-04 -7.497710e-03 1.535360e-03 5.059862e-03 7.991391e-03
[45] -3.221344e-03 1.862683e-05 5.038464e-03 5.634064e-03 -6.183021e-03 -8.808367e-04 -7.386991e-03 -7.853330e-04 -1.408161e-02 5.618191e-03 -4.095731e-03
[56] -5.183360e-03 -1.000162e-03 -5.005816e-03 1.759413e-02 6.441345e-03 3.415269e-03 -4.811727e-03 1.280898e-02 5.689756e-03 1.713828e-02 1.870359e-02
[67] -5.211524e-03 -1.168249e-02 7.233224e-03 2.061144e-02 -2.995458e-03 -3.788613e-03 3.599604e-03 1.947566e-03 3.509358e-03 4.022963e-03 4.163975e-03
[78] 1.103181e-02 -1.085985e-02 -7.597556e-03 -6.481960e-03 -1.348933e-03 3.213561e-03 -1.311227e-03 5.524959e-03
> plot(returns)
Output:
Assignment 2:
1-700 data is available.Predict the data from 701-850,use the GLM estimation using LOGIT analysis for the same.
Output;
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)
Output:
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