Investor Sentiment and the Pricing of Characteristics-Based Factors

Zhuo Chen, Bibo Liu, Huijun Wang, Zhengwei Wang, Jianfeng Yu
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引用次数: 2

Abstract

Using portfolios that are formed by directly sorting stocks based on their exposure to characteristics-based factors, earlier studies find that these beta-sorted portfolios have very large ex post factor beta spreads. However, the return spreads between high- and low-beta firms are typically tiny and insignificant. This study examines the time variation in the pricing of a large set of characteristics-based factors. Our evidence shows a striking two-regime pattern for most of the factor-beta-sorted portfolios: high-beta portfolios earn significantly higher returns than low-beta portfolios following high-sentiment periods, whereas the exact opposite occurs following low-sentiment periods. Remarkably, this two-regime pattern is completely reversed when macro-related factors, such as consumption growth and TFP growth, are used. The evidence based on mutual fund and hedge fund returns also confirms this two-regime pattern. Our findings suggest that the exposure to most of these characteristics-based factors is likely to be a proxy for the level of mispricing, rather than risk, especially during high-sentiment periods.
投资者情绪与基于特征因素的定价
早期的研究发现,通过直接根据基于特征的因素对股票进行分类而形成的投资组合,这些贝塔分类的投资组合具有非常大的事后因素贝塔价差。然而,高贝塔公司和低贝塔公司之间的回报差通常很小且不显著。本研究考察了一组基于特征因素的定价的时间变化。我们的证据显示,对于大多数贝塔因子排序的投资组合来说,存在显著的两种模式:在情绪高涨时期,高贝塔投资组合的回报率明显高于低贝塔投资组合,而在情绪低迷时期,情况恰恰相反。值得注意的是,当使用消费增长和全要素生产率增长等宏观相关因素时,这种两种模式完全相反。基于共同基金和对冲基金回报的证据也证实了这种二元模式。我们的研究结果表明,对大多数这些基于特征的因素的暴露可能是错误定价水平的代表,而不是风险,尤其是在情绪高涨的时期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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