Why Are Some Chinese Firms Failing in the US Capital Markets? A Machine Learning Approach
53 Pages Posted: 2 Aug 2021
Date Written: July 27, 2021
We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the process. We examine the market performance from three different angles: the underpricing (or short-term market phenomena), the post-issuance stock underperformance (or long-term market phenomena), and the regulatory delistings (IPO failure risk). Using machine learning technique that can better handle various data problems, we improve on the predictive power of traditional estimations, such as OLS and logit. Our predictive model highlights some novel findings: failed Chinese companies have chosen unreliable U.S. intermediaries when going public, and they tend to suffer from more severe owners-related agency problems.
Keywords: initial public offerings, machine learning, China, underpricing, delistings, long-run returns, cross-listings, prediction
JEL Classification: C4, C53, C54, G30, G32, G33
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