On Modelling IPO Failure Risk

54 Pages Posted: 17 Mar 2022

See all articles by Gonul Colak

Gonul Colak

University of Sussex ; Hanken School of Economics

Mengchuan Fu

Bentley University

Iftekhar Hasan

Fordham University ; Bank of Finland; University of Sydney

Date Written: February 20, 2022


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 of 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

JEL Classification: C18; C40; C45; G17; G30; G32

Suggested Citation

Colak, Gonul and Fu, Mengchuan and Hasan, Iftekhar, On Modelling IPO Failure Risk (February 20, 2022). Economic Modelling, Forthcoming, Available at SSRN: https://ssrn.com/abstract=4039067

Gonul Colak (Contact Author)

University of Sussex ( email )

Jubilee Building
Brighton, BN1 9SN
United Kingdom

Hanken School of Economics ( email )

P.O. Box 479
Arkadiankatu 22
FI-00100 Helsinki, 00100

Mengchuan Fu

Bentley University ( email )

175 Forest Street
Waltham, MA 02145
United States
02453 (Fax)

Iftekhar Hasan

Fordham University ( email )

NEW YORK, NY 10023
United States

Bank of Finland ( email )

P.O. Box 160
Helsinki 00101

University of Sydney ( email )

P.O. Box H58
Sydney, NSW 2006

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