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 language | English |
|---|---|
| Article number | 105790 |
| Peer-reviewed scientific journal | Economic Modelling |
| Volume | 109 |
| Number of pages | 19 |
| ISSN | 0264-9993 |
| DOIs | |
| Publication status | Published - 04.02.2022 |
| MoE publication type | A1 Journal article - refereed |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 4 Quality Education
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- 511 Economics
- IPOs
- machine learning
- IPO failure risk
- IPO delisting
- gradient boosting
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