Beyond Fama-French Factors: Alpha from Short-Term Signals
研究发现，通过组合短期反转、短期动量、分析师修正、短期风险和月度季节性信号，并采用降低交易成本的买卖规则，投资者可以在全球流动性资产中获得显著的净阿尔法，且该阿尔法与传统的法马-弗伦奇因子不相关。
Short-term alpha signals are generally dismissed in traditional asset pricing models, primarily due to market friction concerns. However, this paper demonstrates that investors can obtain a significant net alpha by applying a combination of signals to a liquid global universe and with advanced buy/sell trading rules that mitigate transaction costs. The composite model consists of short-term reversal, short-term momentum, short-term analyst revisions, short-term risk, and monthly seasonality signals. The resulting alpha is present in out-of-sample and post-publication periods and across regions, translates into long-only applications, is robust to incorporating implementation lags of several days, and is uncorrelated to traditional Fama-French factors.