A Study On Risk Factors Associated With Stock Market

Abstract

The performance of stock markets is an intricate interplay of macroeconomic indicators, structural financial variables, and investor psychology. This paper presents an in-depth study on the multi-dimensional risk factors affecting stock market performance in India. It utilizes a mixed-methods approach that combines econometric models such as GARCH (Generalized Autoregressive Conditional Heteroskedasticity), Fama-MacBeth regression, and Principal Component Analysis (PCA) with primary behavioral data collected through structured surveys of active market participants.

On the macroeconomic front, key indicators including inflation, interest rates, GDP growth, and crude oil prices were evaluated over a 10-year period. Structural variables such as trading volume, liquidity, FII (Foreign Institutional Investor) inflows, and the India VIX index were analyzed using both descriptive and inferential statistical tools. Behaviorally, investor sentiment was measured through survey instruments assessing overconfidence, herd behavior, and loss aversion.

The findings suggest that while macroeconomic indicators significantly shape long-term trends, short-term volatility is predominantly driven by structural liquidity and investor sentiment. For instance, volatility spikes during events such as the COVID-19 market crash showed strong alignment with behavioral biases like herding and overreaction to news. Additionally, PCA revealed that more than 85% of market volatility could be explained by three primary components — macroeconomic risk, structural market variables, and behavioral sentiment.

This study contributes significantly to the existing literature by establishing a holistic, multi-layered framework for understanding stock market dynamics. It proposes actionable insights for policymakers, traders, and investors on how to interpret market risk by considering not just economic data, but also behavioral triggers. The convergence of psychology and financial modeling opens up new avenues for forecasting market movements with improved accuracy and practical relevance in emerging economies.