Fundamental Anomalies
提出一种独立于投资组合的方法估计q理论模型，用贝叶斯MCMC匹配公司级股票收益，发现模型能产生规模、动量等溢价，但无法解释价值和应计异象。
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. This paper was accepted by Lukas Schmid, finance. Funding: S. Wang acknowledges financial support from the National Natural Science Foundation of China [Grants 72373110 and 71902140] and the Fundamental Research Funds for the Central Universities in China. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01313 .