On modeling IPO failure risk

Gonul Colak, Mengchuan Fu and Iftekhar Hasan

Economic Modelling, 2022, vol. 109, issue C

Abstract: This paper offers a novel framework, combining firm operational risk, IPO pricing risk, and market risk, to model IPO failure risk. By analyzing nearly a thousand variables, we observe that prior IPO failure risk models have suffered from a major missing-variable problem. Evidence reveals several key new firm-level determinants, e.g., the volatility operating performance, the size of its accounts payable, pretax income to common equity, total short-term debt, and a few macroeconomic variables such as treasury bill rate, and book-to-market of the DJIA index. These findings have major economic implications. The total value loss from not predicting the imminent failure of an IPO is significantly lower with this proposed model compared to other established models. The IPO investors could have saved around $18billion over the period between 1994 and 2016 by using this model.

Keywords: IPOs; Machine learning; IPO Failure risk; IPO delisting; Gradient boosting (search for similar items in EconPapers)
JEL-codes: C18 C40 C45 G17 G30 G32 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:109:y:2022:i:c:s0264999322000360

DOI: 10.1016/j.econmod.2022.105790

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