A Dynamic Model of Characteristic-Based Return Predictability

• 成果类型: Article
• 署名作者: Alti, Aydogan; Titman, Sheridan
• 署名单位: University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.12839
• 发表日期: 2019
• 页码: 3187-3216
• 关键词: cross-section; excess volatility; momentum; MARKET; stocks; autocorrelation; overconfidence; INVESTMENT; GROWTH

摘要

We present a dynamic model that links characteristic-based return predictability to systematic factors that determine the evolution of firm fundamentals. In the model, an economy-wide disruption process reallocates profits from existing businesses to new projects and thus generates a source of systematic risk for portfolios of firms sorted on value, profitability, and asset growth. If investors are overconfident about their ability to evaluate the disruption climate, these characteristic-sorted portfolios exhibit persistent mispricing. The model generates predictions about the conditional predictability of characteristic-sorted portfolio returns and illustrates how return persistence increases the likelihood of observing characteristic-based anomalies.