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

Gonul Colak, Mengchuan Fu, Iftekhar Hasan*

*Motsvarande författare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

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.
OriginalspråkEngelska
Artikelnummer101331
Referentgranskad vetenskaplig tidskriftPacific-Basin Finance Journal
Volym61
Antal sidor22
ISSN0927-538X
DOI
StatusPublicerad - 17.04.2020
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

Nyckelord

  • 512 Företagsekonomi

Styrkeområden och områden med hög potential (AoS och AoHP)

  • AoS: Finansiering, redovisning och företagsstyrning

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