行业回报的可预测性和反应不足:来自大宗商品市场的证据

Mohammad R. Jahan-Parvar, Andrew Vivian, M. Wohar
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引用次数: 9

摘要

本文发现有显著证据表明,商品日志价格变化可以预测长达六个交易周(30天)的行业水平回报。我们发现,在1985-2010年期间,美国49个行业中有40个行业可以用至少一种商品来预测。我们的发现与Hong和Stein(1999)的“反应不足假说”是一致的。与先前的文献不同,我们通过使用日常数据来确定反应不足的长度。我们对25种商品和49个行业之间的回报联系进行了全面的研究。这提供了一个更详细的调查反应不足和投资者注意力不集中的假设比大多数相关文献。最后,我们实施了数据挖掘稳健方法来评估行业回报对商品对数价格变化的反应的统计显著性,其中贵金属(如黄金)的特征最为突出。虽然我们的结果表明适度的样本外预测能力,但它们证实了商品数据可以提前四个多交易日预测股票回报的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictability and Underreaction in Industry-Level Returns: Evidence from Commodity Markets
This paper finds significant evidence that commodity log price changes can predict industry-level returns for horizons of up to six trading weeks (30 days). We find that for the 1985–2010 period, 40 out of 49 U.S. industries can be predicted by at least one commodity. Our findings are consistent with Hong and Stein’s (1999) “underreaction hypothesis.” Unlike prior literature, we pinpoint the length of underreaction by employing daily data. We provide a comprehensive examination of the return linkages among 25 commodities and 49 industries. This provides a more detailed investigation of underreaction and investor inattention hypotheses than most related literature. Finally, we implement data-mining robust methods to assess the statistical significance of industry returns reactions to commodity log price changes, with precious metals (such as gold) featuring most prominently. While our results indicate modest out-of-sample forecast ability, they confirm evidence that commodity data can predict equity returns more than four trading weeks ahead.
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