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Classical Linear Regression Analysis

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To validate the data, one of the main assumptions of the Classical Linear Regression Model i.e. Multicollinearity was checked by using the correlation matrix.
Table 5.2.1 – Correlation Matrix 1 (Pearson, Kendall and Spearman) In the above matrix it can be observed that there is presence of Multicollinearity because of high correlation between, M3, Gold Price and WPI and Index of Industrial Production therefore as a remedial measure the above mentioned variables are removed from the analysis.
A correlation matrix is again constructed between Sensex, Exchange Rate, Silver Price, Call Money Rates and Foreign Exchange Rate to check if the assumption of no multicollinearity is satisfied.
Table 5.2.2 – Correlation Matrix 2 (Pearson, Kendall, and …show more content…
5.2.1 – Linear Multiple Regression Analysis
To fulfil the objective the following hypothesis are made:
1) H0: There is no significant relationship between BSE Sensex and Exchange Rate
H1: There is a significant relationship between BSE Sensex and Exchange Rate
2) H0: There is no significant relationship between BSE Sensex and Silver Prices
H1: There is a significant relationship between BSE Sensex and Silver Prices
3) H0: There is no significant relationship between BSE Sensex and Call Money Rates
H1: There is a significant relationship between BSE Sensex and Call Money Rates
4) H0: There is no significant relationship between BSE Sensex and Foreign Exchange Reserves
H1: There is a significant relationship between BSE Sensex and Foreign Exchange Reserves
Model: BSE Sensex = b0 + b1 Exchange Rate + b2 Silver Prices + b3 Call Money Rates + b4 Foreign Exchange …show more content…
Since the R Square is 0.7947, we can conclude that 79% of variations in BSE Sensex Index is being explained by Exchange rate, Call Money Rates and Foreign Exchange Reserves with a positive relation between BSE Sensex Index and Foreign Exchange Reserves and negative relation between BSE Sensex Index with Exchange rate and Call Money Rates. Hence, this model is a good fit.
The data therefore highlights that out of a total of eight variables that were under consideration, only Exchange Rates, Call Money Rates and Foreign Exchange Reserves significantly affects the performance of Indian Stock Market. Therefore, we reject the null hypothesis for the above mentioned significant variables and fail to reject for other macro-economic variables.
Final Model – 80793.021 - 1723.012 Exchange Rate – 3489.217 Call Money Rates + 3.466 Foreign Exchange

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