Confident Risk Premiums and Investments Using Machine Learning Uncertainties
基于多种线性和机器学习模型，为股票风险溢价预测推导出事前置信区间，并利用预测精度的横截面差异，提出改进的“置信高低”投资策略，该策略在样本外表现优于传统策略。
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.