This paper investigates the low risk anomaly, which suggests that less risky stocks outperform riskier ones. Focusing on the European stock market, the present study examines the influence of coskewness, a measure of asymmetry in stock returns with respect to the market return. Stocks are sorted into 2x5 quintile portfolios based on coskewness and beta volatility. Regression analysis using Fama-French three and five factor models reveals a significant low risk anomaly in the low coskewness category, where less risky portfolios consistently outperform riskier ones. However, as coskewness increases, the low risk anomaly weakens and loses significance. In the high coskewness category, less risky portfolios no longer consistently outperform riskier ones. In other words, accounting for coskewness significantly lowers the profitability of low risk and betting-against-beta strategies in Europe. These findings enhance the understanding of the relationship between risk and returns in the European market.
Empirical findings that less risky stocks consistently outperform the riskier ones have motivated a great number of studies on the low risk anomaly. This paper aims to explain it away by controlling for coskewness of stock returns with the market return on the European stock market represented by constituents of the S&P 350 Index. Stocks are double sorted on coskewness and beta volatility into 2x5 quintile portfolios, and their excess returns are subsequently regressed on the Fama-French three and five factor models separately for both coskewness categories. In the low coskewness category, a persistent, highly significant low risk anomaly is identified. As the coskewness increases, the low risk anomaly dramatically decreases and loses statistical significance. As a result, in the high coskewness category, less risky portfolios no longer consistently outperform the riskier ones. Results demonstrate that accounting for coskewness in the model remarkably decreases the profitability of low risk and betting-against-beta strategies in European data.
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