Fundamental Analysis of Chinese Stock Market
45 Pages Posted: 10 May 2019
Date Written: April 12, 2019
I generate a fundamental signal library with more than 8000 fundamental signals by considering various combinations of the accounting variables in Chinese stock market. I take two standard approaches, time-series intercept tests and cross-sectional coeﬃcient tests, to identify anomalies from this signal library. I ﬁnd that 142 signals can pass both of the two tests even after accounting for sample variation with bootstrap. I also take several aggregation techniques to extract information from these 142 signals and ﬁnd that PCA (Principal Component Analysis) performs best. Furthermore, I construct a new factor based on the 142 signals and augment the Fama-French three-factor model to form a four-factor model (A4). The four-factor model performs better than Fama-French three-factor model, Carhart four-factor model, Q4 factor model, Fama-French ﬁve-factor model and at least as well as Fama-French six-factor model in terms of accomodating the hedge portfolio returns of over 8000 fundamental signals.
Keywords: Data-Mining, Anomalies, Cross-Section of Returns, Chinese Stock Market
JEL Classification: G10
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