Behavioral Risk Measurement: Empirical Evidence From NYSE

Abstract

This study aims to create a risk measure based on systematic investor behavior. For this purpose, as an alternative to the classical risk measure, volatility, the empirical validity of the downside risk measure, which includes skewness and kurtosis values, was tested. Standard deviation, skewness, and kurtosis differences are used to explain the returns of portfolios created using data from stocks listed on the New York Stock Exchange (NYSE) between 1982 and 2020 depending on different risk concepts. Risk definitions are based on the previous period's skewness and kurtosis coefficients of stock returns. Based on the determined measures, stocks are classified according to their risk level. The relationship between returns and risk measures was examined by regression analysis. According to the results, negative skewness did not provide a higher return than positive skewness. In addition, a higher kurtosis value did not provide higher returns than a lower kurtosis value. As a result, the concept of risk, which represents the loss of the investor, emerges as a result of irrational systematic investor behavior and can be modeled with the skewness coefficient of the return distribution. However, taking a risk in this sense does not promise a reward.