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Data snooping in equity premium prediction
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DOI：10.1016/j.ijforecast.2020.03.002.png)
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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. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Equity premium
Prediction
Data snooping
Multiple testing
Return predictability
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