Impact of Macroeconomic Indicators on the Indian Stock Market: A Study on NSE Nifty

It is impossible to overstate the importance of the financial sector to the economy. Since the economy of India has opened its gates to foreign direct investment (FDI), a lot of money has come into the country. This has influenced the stock market. This study examines the relationship between the prices of stocks and the key economic indicators for India. This analysis focuses on the effects of foreign direct investment (FDI) as a proportion of real gross domestic product (RGDP), money supply (M3) as a proportion of RGDP, gold price, exchange rate, and consumer price index (CPI) on the movement of the stock market (NSE Nifty). The CPI has frequently been used to assess a country's inflation rate. The analysis examines 36 quarters of data, beginning in 2012-2013 and ending in 2020-2021. To figure out what the data mean, the study used descriptive statistics, a test for normality, multiple regression analysis, and a chi-square test for goodness of fit. Specifically, there are two levels of analysis. In the first step, data from the 2012-2013 financial year to the 2019-2020 financial year were put through a process called multiple regression. The second step is to use the model to predict what will happen in 2020-2021 and then do a chi-squared test to see how accurate it is. NSE stock price was regressed on Foreign direct investment as a share of RGDP, money supply M3 as a share of RGDP, the exchange rate, the price of gold, and the consumer price index. Except for the CPI, all of the control factors have a negative effect on the price of NSE stock. This was found by using regression analysis. Other than the consumer price index and the exchange rate, however, none of the other indicators have a significant impact on the stock market. The adjusted R-squared statistic and the F-test show that the model is statistically important. Actual NSE stock prices for the first three quarters of 2020-21 are roughly what was expected, according to the chi square test.