更高的矩和美国行业回报:实现偏态和峰度

IF 3.6 Q1 BUSINESS, FINANCE
Xiaoyu Chen, Bin Li, A. Worthington
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引用次数: 4

摘要

目的本文的目的是在行业层面上检验收益的高阶矩(已实现的偏度和峰度)与后续收益之间的关系,重点是实证可预测性和通过交易策略的实际应用。设计/方法/方法Kenneth French数据库中1970-2019年期间48个美国行业的每日收益用于计算较高时刻,并构建短期和中期单一交易策略。该分析调整了常见风险因素(市场、规模、价值、投资、盈利能力和非流动性)的回报,以确认传统资产定价模型是否能够捕捉到这些关系。发现过去的偏度与随后的行业回报呈正相关,这种关系无法通过常见的风险因素来解释。还有一种时变效应,即在商业周期扩张中,偏度的预测作用比衰退强得多,这一结果与不同的投资者乐观情绪一致。然而,峰度与随后的行业回报之间没有显著的关系。该分析使用价值加权收益和相等加权收益来确认稳健性。研究局限性/含义实现时刻的计算通常使用高频日内数据,遗憾的是,行业无法获得。此外,所选择的投资组合排序方法可能会省略一些信息,因为它只比较平均组回报。尽管如此,行业层面的偏度和未来回报之间的密切关系表明,回报的变化是常见风险因素无法解释的。这丰富了对市场异常的了解,并再次提出了市场效率和传统资产定价模型有效性不足的问题。一个建议是,通过将行业倾斜度作为风险因素,可以显著改进现有的多因素资产定价模型。实际含义考虑到行业层面的偏斜与未来回报之间的关系,投资者可以预测后续的行业回报,以选择表现更好的基金。他们甚至可能构建基于回报分布的交易策略,从而产生异常回报。此外,由于对个股的评估也包含行业信息,而业绩较好行业的股票回报率较高,因此与行业回报分布相关的风险也可以揭示个股的选择。原创性/价值虽然在公司层面有大量证据表明较高时刻与未来回报之间存在关系,但在行业层面却很少。此外,通过测试行业高点和未来回报之间的关系是否存在时间变化,本文得出了关于股票回报可预测性在商业周期中的不对称效应的新证据。最后,该分析补充了公司层面的结果,只关注更高矩的分解分量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Higher moments and US industry returns: realized skewness and kurtosis
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.
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