Month-of-the-year Effect in Indian Equity Market: A Behavioural Finance Approach using Multi-Model Evidence (2015-2024)

This study examines seasonal trends in the Indian stock market, focusing on the BSE Sensex and NSENifty 50, to identify month-of-the-year effects in a post-COVID emerging-market context and assess implicationsfor market efficiency. Using monthly closing prices from January 2015 to December 2024, we apply a multimodel framework: Zivot–Andrews, ERS-DF-GLS, and Ng–Perron tests for stationarity; ARIMA/SARIMA forseasonality and autocorrelation; GARCH/EGARCH for volatility clustering and asymmetry; and OLS dummyregression for monthly return differentials. Results show mildly positive February and May effects and a strongJuly effect in the Sensex, while the Nifty 50 records a significant negative March effect. These anomalies persistafter adjusting for autocorrelation and volatility asymmetry. The findings offer insights for investors, portfoliomanagers, and policymakers, highlighting the role of behavioral factors in Indian markets during a decade shapedby demonetisation, COVID-19, and post-pandemic recovery, thus contributing to behavioral finance literature onSouth Asian emerging markets.