TY - JOUR
T1 - On modeling IPO failure risk
AU - Colak, Gonul
AU - Fu, Mengchuan
AU - Hasan, Iftekhar
N1 - Funding Information:
Authors are grateful to the Editor (Sushanta Mallick) and an anonymous reviewer for significant inputs and guidance. Iftekhar Hasan acknowledges the support of ARC Discovery Grant , DP210102611, Australia . Usual caveats apply.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/2/4
Y1 - 2022/2/4
N2 - 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.
AB - 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.
KW - 511 Economics
KW - IPOs
KW - machine learning
KW - IPO failure risk
KW - IPO delisting
KW - gradient boosting
UR - http://www.scopus.com/inward/record.url?scp=85124421011&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/e29125d5-bab4-3d9a-a951-709ee5995765/
U2 - 10.1016/j.econmod.2022.105790
DO - 10.1016/j.econmod.2022.105790
M3 - Article
SN - 0264-9993
VL - 109
JO - Economic Modelling
JF - Economic Modelling
M1 - 105790
ER -