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Why are some Chinese firms failing in the US capital markets? A machine learning approach

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journal contribution
posted on 2023-06-10, 02:41 authored by Gonal ColakGonal Colak, Mengchuan Fu, Iftekhar Hasan
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.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Pacific Basin Finance Journal

ISSN

0927-538X

Publisher

Elsevier

Volume

61

Page range

1-22

Article number

a101331

Department affiliated with

  • Accounting and Finance Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2022-02-22

First Open Access (FOA) Date

2022-02-22

First Compliant Deposit (FCD) Date

2022-02-21

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