Replicating and Digesting Anomalies in the Chinese A-Share Market

We replicate 469 anomaly variables similar to those studied by Hou et al. (2020) using Chinese A-share data and a reliable testing procedure with mainboard breakpoints and value-weighted returns. We find that 83.37% of the anomaly variables do not generate significant high-minus-low quintile raw return spreads. Further adjusting risk increases the failure rate slightly to 84.22% based on CAPM alphas and 86.99% based on Fama–French three-factor alphas. We show that the conventional procedure using all A-share breakpoints with equal-weighted returns for the anomaly test is indeed problematic as it assigns too much weight to microcaps and has a very limited investment capacity. The CH3-factor, CH4-factor, and q-factor models show the best performance over the whole sample period. The q-factor model is the best performer in the post-2007 subsample period after significant improvements occurred in China’s financial market environment, such as the completion of the split-share structure reform and the implementation of new accounting standards conforming to the International Financial Reporting Standards. The non–state-owned enterprise subsample in the post-2007 period is a cleaner sample in which the CH4-factor and q-factor models are the best performers. This paper was accepted by Lukas Schmid, finance. Funding: Z. Li acknowledges financial support from the National Natural Science Foundation of China [Grant 72103043] and the Fundamental Research Funds for the Central Universities in UIBE [Grants 19QN01 and 22PY053-72103043]. L. X. Liu acknowledges financial support from the National Natural Science Foundation of China [Grants 71872006 and 72273006]. L. X. Liu and X. Liu acknowledge financial support from the Guanghua Thought Leadership Platform of Peking University. K. C. J. Wei acknowledges partial financial support from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant 15507320]. The authors acknowledge financial support from the Guanghua School of Management, Peking University; the School of Banking and Finance, University of International Business and Economics; and the Non-PAIR Research Centre “Research Centre for Quantitative Finance” at Hong Kong Polytechnic University. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4904 .