Kurtosis is a statistical measure used to describe the shape of a data distribution, specifically focusing on the ìtailednessî or how much of the data lies in the tails compared to the normal distribution. In finance and investing, kurtosis helps investors understand the probability of extreme returnsóboth high gains and steep lossesóin a stock, index, or portfolio.

At its core, kurtosis indicates the likelihood of outliers. A normal distribution has a kurtosis value of 3, known as mesokurtic. If the kurtosis is greater than 3, it is called leptokurtic, meaning the distribution has fatter tails and a higher chance of extreme eventsóthis is often seen in volatile markets. On the other hand, a kurtosis value less than 3, known as platykurtic, suggests thinner tails and fewer outliers, indicating relatively stable price movements.

For investors and risk analysts, understanding kurtosis is crucial when evaluating the risk-return profile of investments. High kurtosis may signal that a portfolio is exposed to sudden market shocks or rare but impactful price swings. Conversely, low kurtosis suggests steadier performance but potentially limited upside opportunities. Hence, kurtosis complements other statistical measures like mean, variance, and skewness in building a more comprehensive view of market behavior.

In practical terms, analysts use kurtosis while conducting risk management and portfolio diversification studies. It helps determine whether returns follow normal patterns or exhibit fat-tailed risks that could lead to unexpected outcomes. By analyzing kurtosis, investors can make more informed decisions and better prepare for extreme market eventsóaligning with sound, data-driven investment practices encouraged by SEBI.