Confident Risk Premiums and Investments Using Machine Learning Uncertainties
Abstract This paper derives ex-ante confidence intervals for 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.
- DOI
- 10.1093/rfs/hhaf087
- Volume
- 39 (5)
- Pages
- 1463-1505
- Language
- en
- Export
- BibTeX
- Sources
- openalex crossref