Data Snooping in Equity Premium Prediction

H. Dichtl, W. Drobetz, A. Neuhierl, Viktoria-Sophie Wendt
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引用次数: 9

Abstract

Abstract We analyze the performance of a comprehensive set of equity premium forecasting strategies. All strategies were found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Welch and Goyal (2008) in that almost all equity premium forecasts fail to beat the mean out-of-sample. Only few forecasting strategies that are based on Ferreira and Santa-Clara’s (2011) sum-of-the-parts approach generate robust and statistically significant economic gains relative to the historical mean even after controlling for data snooping and accounting for transaction costs.
股票溢价预测中的数据窥探
摘要本文分析了一套综合的股票溢价预测策略。在以前的学术出版物中,所有策略的表现都优于平均值。然而,使用多重测试框架来解释数据窥探,我们的发现支持Welch和Goyal(2008),几乎所有的股票溢价预测都不能超过样本外均值。只有少数基于Ferreira和Santa-Clara(2011)的部分求和方法的预测策略,即使在控制数据窥探和考虑交易成本之后,相对于历史均值,也能产生稳健的、统计上显著的经济收益。
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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