股票特征和股票收益的实际应用:对预期收益横截面的怀疑

Bradford Cornell
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摘要

在《投资组合管理杂志》2020年7月的多资产特刊《股票特征和股票回报:对预期回报横截面持怀疑态度的人》一文中,加州大学洛杉矶分校(UCLA)的布拉德福德·康奈尔(Bradford Cornell)对股票特征和预期回报之间相关性的可靠性以及投资效用提出了质疑。对于这些关系,我们究竟能了解多少呢?他的回答是,很少。因为这些特征不会持续存在或可预测地重复出现,任何观察到的它们与跨资产类别平均回报的未来变化之间的相关性只有有限的实际用途。康奈尔指出,有几个障碍会破坏股票特征作为回报预测指标的可靠性,包括非持续性、模型不确定性、数据窥探,尤其是非平稳性。这些情况使投资者难以辨别回报的真正驱动因素,也难以自信地预测回报和相对的未来风险。作者建议市场参与者警惕包括智能贝塔在内的投资方法,这些方法假设特征与未来回报之间存在强大的相关性。主题:投资组合管理/多资产配置,绩效评估
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Practical Applications of Stock Characteristics and Stock Returns: A Skeptic’s Look at the Cross Section of Expected Returns
In Stock Characteristics and Stock Returns: A Skeptic’s Look at the Cross Section of Expected Returns, in the July 2020 multi-asset special issue of The Journal of Portfolio Management, Bradford Cornell of the University of California in Los Angeles (UCLA) questions the dependability, and thus the investment utility, of correlations between stock characteristics and anticipated returns. How much can really be known about these relationships? His answer is, very little. Because these characteristics do not persist or recur predictably, any observed correlation between them and the future changes in average returns across asset classes has only limited practical use. Cornell identifies several impediments that undermine the reliability of stock characteristics as predictors of returns—including nonpersistence, model uncertainty, data snooping, and, especially, nonstationarity. These conditions make it difficult for investors to discern the real drivers of returns and to confidently forecast returns and relative future risk. The author advises market participants to be wary of investment approaches, including smart beta, that assume robust correlations between characteristics and future returns. TOPICS: Portfolio management/multiasset allocation, performance measurement
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