Conference Agenda
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Session 506
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Confident Risk Premiums and Investments using Machine Learning Uncertainties University of Houston, United States of America This paper derives ex-ante confidence intervals of stock risk premium forecasts that are based on a wide range of linear and Machine Learning models. Exploiting the cross-sectional variation in 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 outperform traditional high-low strategies in delivering superior out-of-sample returns and Sharpe ratios across all models. The outperformance increases (decreases) with the model complexity (bias). The confident-high-low strategies are economically interpretable as trading strategies of ambiguity-averse investors who account for confidence intervals around risk premium forecasts. | ||