Confident Risk Premiums and Investments using Machine Learning Uncertainties
80 Pages Posted: 18 Nov 2021 Last revised: 8 Dec 2021
Date Written: November 16, 2021
This paper derives ex-ante (co)variances of stock-level and portfolio-level risk premium predictions from neural networks (NNs). Based on the precision of risk premium forecasts, I provide improved investment strategies. The confident high-low strategies that take long-short positions exclusively on stocks with precise risk premium forecasts deliver superior out-of-sample returns and Sharpe ratios than traditional high-low strategies. Mean-variance strategies that incorporate covariances of return predictions also outperform existing strategies. Risk premium variances reflect time-varying market uncertainty and spike after financial shocks. Cross-sectionally, the level and precision of risk premiums are correlated, thus NN-based investments deliver more gains in the long positions.
Keywords: Stock Return Predictions, Neural Networks, Standard Errors, Risk Premiums, Confidence Intervals, Investment Strategies, Machine Learning Uncertainties
JEL Classification: G11, C13, C57, C58
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