Higher moments and US industry returns: realized skewness and kurtosis

Abstract

Purpose The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies. Design/methodology/approach Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships. Findings Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns. Research limitations/implications The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor. Practical implications Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking. Originality/value While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

Realised volatility and industry momentum returns

“…Although the recent work by Badreddine and Clark (2021) uses industry-adjusted volatility to explain individual momentum returns in the UK, no study to our best knowledge has investigated this for industry momentum. As discussed, stock returns in the same industry display comovements (Parsons et al, 2020;Chen et al, 2021Chen et al, , 2022. Industry portfolios with higher gains also exhibit higher risk than other well-diversified trading portfolios (Badreddine and Clark, 2021).…”

“…Volatility. Following Chen et al (2021), we transform the raw industry returns into log returns to reflect the dollar value of the returns generated by investors over multiple investment horizons:…”

“…Although unsystematic risks theoretically should be diversified away in an efficient market, markets are sometimes inefficient. More importantly, stock returns in the same industry show comovements, so industry portfolios are normally undiversified (Parsons et al, 2020;Chen et al, 2021Chen et al, , 2022. We therefore decompose the realised total volatility into idiosyncratic and systematic volatility to further examine which component better drives the relationship between volatility and industry momentum returns.…”

“…Daily data on value-weighted industry returns of the 48 industry portfolios in the US stock market is sourced from the Kenneth French Data Library. This data has several advantages, including a long sample period to allow for better stability and unbiasedness of the estimates and widespread use to facilitate comparisons with existing work (Stivers and Sun, 2010;Chen et al, 2021Chen et al, , 2022. The sample period starts in July 1969 and ends in June 2021.…”

“…We also know that stocks within the same industry have similar asset profiles and capital structures, employ comparable production technologies, and operate in like economic, financial, and regulatory environments. These can also translate to similar return distributions and comovements (Parsons et al, 2020;Chen et al, 2021Chen et al, , 2022. Compared with volatility calculated using individual data, industry volatility would then capture risks common to all stocks in the same industry (Wu and Mazouz, 2016).…”