Factors Affecting NIFTY 50 and its Returns
Faculty & Mentor: Veerendra Anchan, Assistant Professor, ASMSOC, NMIMS University
Student Info: TYBBA F
1) Dhairya Ajmera, TYBBA F013, ASMSOC, NMIMS University, Mumbai
2) Harshit Jasrasaria, TYBBA F019, ASMSOC, NMIMS University, Mumbai
3) Parth Rajpal, TYBBA F034, ASMSOC, NMIMS University, Mumbai
4) Shivansh Arora, TYBBA F047, ASMSOC, NMIMS University, Mumbai
5) Uday Ahuja, TYBBA F054, ASMSOC, NMIMS University, Mumbai
Abstract
The key objective of the present study is to explore the impact of different macroeconomic variables on the stock prices in India using annual data from 2014-15 to 2022-23.
A multiple regression model is designed to test the effects of macroeconomic variables on the stock prices and granger causality test is conducted to examine whether there exists any causal linkage between stock prices and macro-economic variables.
The project will use a variety of methods to analyze the impact of these factors on Nifty returns. These methods may include regression analysis, seasonality regression, Johnasens Cointegration, Unit Root Tetsts and ANNOVA. This study provides evidence of a variation between Nifty stock returns and the values of various global indices. This finding has implications for investors and policymakers. Investors should be aware of the relationship between Nifty stock returns and global indices when making investment decisions.
The Johansen test determines if two or more non-stationary time series are cointegrated. Augmented Dickey-Fuller test was also conducted to test whether the data was non stationery or not. Cointegration is a time series quality that indicates that they have a long-run equilibrium relationship even if they are not stationary in the short run.
Through regression we try to understand if macro economic factors like currency conversion rate and the returns of indices of foreign countries have an impact on the NIFTY 50, whereas through seasonality regression we try to determine if NIFTY gets affected due to seasonal trends in different quarter or months.
