On modeling IPO failure risk

Gonul Colak, Mengchuan Fu, Iftekhar Hasan*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper offers a novel framework, combining firm operational risk, IPO pricing risk, and market risk, to model IPO failure risk. 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.
Original languageEnglish
Article number105790
Peer-reviewed scientific journalEconomic Modelling
Volume109
Number of pages19
ISSN0264-9993
DOIs
Publication statusPublished - 04.02.2022
MoE publication typeA1 Journal article - refereed

Keywords

  • 511 Economics
  • IPOs
  • machine learning
  • IPO failure risk
  • IPO delisting
  • gradient boosting

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

  • AoS: Financial management, accounting, and governance

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