06/02/2026
Dr. Prashant Joshi, Associate Professor of Finance, recently published an article on his blog, "Finance, Decoded," examining the limitations of the traditional “bell curve” assumption in stock market analysis. Drawing on S&P 500 daily return data, Dr. Joshi demonstrates that financial markets exhibit fat tails, negative skewness, and volatility clustering, meaning that extreme market gains and losses occur much more frequently than standard risk models predict. While the normal distribution may adequately describe average trading days, it significantly understates the probability and impact of major market events, particularly severe downturns. His analysis finds that alternative models, such as the Student’s t-distribution and Generalized Error Distribution (GED), provide a more realistic representation of market behavior by accounting for heavier tails. The article further highlights how reliance on normal-distribution assumptions can lead investors to underestimate drawdown risk, take on excessive leverage, and be unprepared for market crises such as the 2020 COVID-19 downturn. Dr. Joshi concludes by encouraging investors to adopt a more robust, tail-aware approach to risk management through the use of measures such as Expected Shortfall (ES), rigorous stress testing, prudent position sizing, and adequate liquidity reserves to better withstand rare but consequential market shocks. The article can be accessed at
https://joshiprash.blogspot.com/2026/03/the-stock-market-isnt-bell-curve-and.html?m=0