Fundamental Anomalies

• 成果类型: Article; Early Access
• 署名作者: Li, Erica X. N.; Ma, Guoliang; Wang, Shujing; Yu, Cindy
• 署名单位: Xiamen University; Xiamen University; Tongji University; Iowa State University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.01313
• 发表日期: 2025
• 关键词: Q-theory; Bayesian MCMC estimation; anomalies; INVESTMENT; profitability

摘要

This paper proposes a portfolio-independent method to estimate q-theory models, in which parameters are obtained using Bayesian Markov chain Monte Carlo (MCMC) to match firm-level stock returns. Our methodology addresses a previous critique on prior studies that model parameters are chosen to fit a specific set of anomalies and different values are needed to fit each anomaly. By targeting the entire sample of firm-level returns and allowing industry and time variations in parameter values, our estimations yield higher correlations between realized and fundamental portfolio returns compared with prior literature. Additionally, the estimated two-capital model generates large and significant size, momentum, profitability, investment, and intangibles premiums, but falls short in explaining the value and accruals anomalies. This limitation underscores the importance of portfolio-independent parameter estimation in evaluating a model's capability to generate return anomalies.