Business App IT Lab - Session 5

Returns and Forecasting

Objective 1 : Find returns of NSE data > 6 months.having selected the 10th data as start and 95th data point as end.Also plot the assignment .

Solution:

Step 1: Read data  in the form of CSV file for the period 1/12/2011 to 5/02/2013
Command:
 z<-read.csv(file.choose(),header=T)

Step 2: Choose the Close column.
Command:
 close<-z$Close

Step 3: Vectorised the data i.e form a matrix of order 1X298 as 298 data points are available in close.
Command:
dim(close)<-c(1,298)

Step 4: Create time-series objects for close data from element (1,10 to1,95)
Command:
close.ts<-ts(close[1,10:95],deltat=1/252)

Step 5: Calculate difference between preceding and succeeding value
Command:
close.diff<-diff(close.ts)

Step 6: Calculate return :
Command:
return<-close.diff/lag(close.ts,k=-1)
final<-cbind(close.ts,close.diff,return)

Step 7: Plot
Command:
plot(return,main="Return from 10th to 95th")
plot(final,main="Data from 10th to 95, Difference, Return")

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

Solution:

Step 1: Read data  in the form of CSV file

Command:

z<-read.csv(file.choose(),header=T)

Step 2: Check the dimension of z

Command

dim(z)

Step 3: Choose 1-700 data

Command

 new<-z[1:700,1:9]

Step 4:

Command

head(new)

Step 5:

Identify the factor and run the Logit regression 

Command

 new$ed <- factor(new$ed)

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

 summary(new.est)

Step 6:

Prediction<-z[701:850,1:8]

 Prediction$ed<-factor(Prediction$ed)

 Prediction$prob<-predict(new.est, newdata =Prediction, type = "response")

 head(Prediction)