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

Gonul Colak, Mengchuan Fu, Iftekhar Hasan*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)


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.
Original languageEnglish
Article number101331
Peer-reviewed scientific journalPacific-Basin Finance Journal
Number of pages22
Publication statusPublished - 17.04.2020
MoE publication typeA1 Journal article - refereed


  • 512 Business and Management
  • machine learning
  • initial public offerings
  • China
  • underpricing
  • delistings
  • long-run returns
  • cross-listings
  • prediction

Areas of Strength and Areas of High Potential (AoS and AoHP)

  • AoS: Financial management, accounting, and governance


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