---
title: Corporate Bond IPO Underpricing
url: https://www.ml-quant.com/papers/ssrn/4825082/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4825082
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4825082
featured: 2024-05-15
citations: unknown
topic: Macro-Finance & Rates
---


# Corporate Bond IPO Underpricing

Bond IPO underpricing is common and rises during times of market uncertainty, suggesting underwriters struggle to estimate asset value in volatile periods.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4825082
- Identifier: SSRN 4825082
- Released: 2022-07-24
- First featured: Quant Letter No. 49 (2024-05-15): https://www.ml-quant.com/issues/2024-05-15/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Macro-Finance & Rates

## Related

- [Corporate Bond Factors: Replication Failures and a New Framework](https://www.ml-quant.com/papers/ssrn/4586652/): The study criticizes inconsistent methodologies in corporate bond factors literature, suggesting a robust factor construction and a clean database for corporate bond returns.
- [Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning](https://www.ml-quant.com/papers/arxiv/2502.01495/): The research applies quantum cognition machine learning to distance metric learning in corporate bond markets, outperforming traditional models in high-yield markets and performing similarly or better in investment grade markets.
- [Deep Learning for Corporate Bonds](https://www.ml-quant.com/papers/ssrn/4527372/): A U.S. corporate bonds market asset pricing model shows that maximizing the Sharpe ratio performs better for individual bonds, with significant excess returns shown in out-of-sample annual SDF portfolio Sharpe ratios.
- [ML for Trade Direction in Corporate Bonds](https://www.ml-quant.com/papers/repec/kap-rqfnac-v-63-y-2024-i-1-d-10-1007-s11156-024-01252-w/): Machine learning can enhance trade direction classification in corporate bond markets, with trade timing and information environment impacting the accuracy of existing rules.
- [Predicting Individual Corporate Bond Returns](https://www.ml-quant.com/papers/ssrn/4753422/): Machine learning, particularly Random Forest, shows strong evidence of return predictability and investment gains for individual corporate bonds, especially in private bonds.
- [Corporate Bond Valuation Factors](https://www.ml-quant.com/papers/ssrn/4751242/): Corporate bond credit spreads are affected by default risk and convenience services, with the European Central Bank's corporate quantitative easing programs significantly impacting corporate bonds' convenience yields.
