Parikalpana KIIT Journal of Management
  • Year: 2024
  • Volume: 20
  • Issue: 1

Analysing day-of-week end effects in the FMCG sector: Angarch and egarch model approach

1CEBA, University of Technology and Applied Sciences, Nizwa, Sultanate of Oman, pk.ns87@gmail.com

2Department of Commerce, Loyola College, Chennai-34, smithasree23@gmail.com

3Department of Commerce, Loyola College, Chennai-34, minimathew206@gmail.com

4Department of Commerce, Loyola College, Chennai-34, prof.mathankumar@gmail.com

Online published on 15 September, 2025.

Abstract

The financial environment has undergone significant change as a result of global economic issues. In this context, understanding the complexity of stock market activity is critical. The Fast-Moving Consumer Goods (FMCG) industry, which is extremely integrated into daily life, offers a distinct and understudied arena for investigating weekend effects anomalies.

This study is useful to investors and experts as they navigate the volatility in a specific sector of the Indian stock market.

A diverse set of five companies with a high volume of stock market activity, FMCG shares, and data are systematically evaluated from July 2008 to June 2023. To investigate the influence of different weekends on stock returns and volatility, GARCH, EGARCH, and linear regression models were utilised.

Mondays have a little higher return trend, but skewed return distributions emphasise the importance of risk management. The “Monday” and “Friday Effects” are statistically significant. The influence of weekends on returns differs for every FMCG company, providing significant information for future trading strategies.

The study will deliver data-driven insights to stakeholders, helping them to make better-informed decisions, particularly when navigating the volatile Indian stock market.

It highlights the need for sensible investing and risk management while also acknowledging that the impact of weekends on returns differs for every FMCG company.

Keywords

Calendar anomalies, EGARCH, FMCG, GARCH, Volatility Patterns