How to Dominate the Historical Average

• 成果类型: Article
• 署名作者: Li, Kai; Li, Yingying; Lyu, Changlei; Yu, Jialin
• 署名单位: Peking University; Peking University Shenzhen Graduate School (PKU Shenzhen); Hong Kong University of Science & Technology; Shanghai University of Finance & Economics
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaf010
• 发表日期: 2025
• 页码: 3086
• 关键词: BOOK-TO-MARKET; stock returns; PREDICTING RETURNS; sample; predictability; inflation; forecasts; inference; earnings; models

摘要

We present a novel methodology for the out-of-sample forecast of the equity premium. Our predictive slope coefficient is a conservative constant that has a lower bias than the zero slope employed by the historical average, but has the same variance. We demonstrate that, theoretically and empirically, our method dominates the historical average in forecast performance. Our methodology establishes a simple yet powerful paradigm for exploiting the real-time equity premium predictability derived from a predictor. Applications of our method reveal that many predictors can forecast the equity premium, and that parameter estimates in previous studies add value to out-of-sample forecasts.