Why are some Chinese firms failing in the US capital markets? A machine learning approach
Gonul Colak,
Mengchuan Fu and
Iftekhar Hasan
Pacific-Basin Finance Journal, 2020, vol. 61, issue C
Abstract:
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 techniques 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 (search for similar items in EconPapers)
JEL-codes: C4 C53 C54 G30 G32 G33 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:61:y:2020:i:c:s0927538x20300822
DOI: 10.1016/j.pacfin.2020.101331
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