Exploring Returns and Logit

#this post is created as a solution for assignments given on 05/02/2013 in IT & Business Applications Lab, Spring Semester, VGSoM, IIT Kharagpur Class of 2014.

Assignment #1: Find returns of NSE data of greater than 6 months having selected the 10th data point as start and 95th data point as end.

Solution: 

Steps:

1. Extract the data (from 5/7/2011 to 7/2/2013) in a separate .csv file.
2. Find the Returns applying Time series and Lag
3. Draw the plot.

Commands used:
z<-read.csv(file.choose(),header=T)
Closedata<-z$Close
Close.ts<-ts(Closedata)
Close.ts<-ts(Closedata,deltat= 1/252)
znew<-ts(data=Close.ts[10:95],frequency=1,deltat=1/252)
znew.ts<-ts(znew)
znew.diff<-diff(znew)
zdenominator<-lag(znew.ts,K=-1)
Returndata<-znew.diff/zdenominator
plot(Returndata,main=" Returndata for 10 th to 95th day of NSE data downloaded ")

Snapshot of Result

Assignment #2: 1-700 data is available, Predict the data from 701-850, use the GLM estimation using LOGIT Analysis for the same.

Solution:

Commands:

  z<-read.csv(file.choose(),header=T)

  z1<-z[1:700,1:9]

  head(z1)

  z1$ed<-factor(z1$ed)

  z1.est<-glm(default ~ age + ed + employ + address + income + debtinc + creddebt + othedebt,  data=z1, family ="binomial")

 summary(z1.est)

 forecast<-z[701:850,1:8]

 forecast$ed<-factor(forecast$ed)

 forecast$probability<-predict(z1.est,newdata=forecast,type="response")

 head(forecast)