Textual Information and IPO Underpricing: A Machine Learning Approach
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
This study examines the predictive power of textual information from S-1 filings in explain-ing initial public offering (IPO) underpricing. The authors’ approach differs from previous research because they utilize several machine learning algorithms to predict whether an IPO will be underpriced or not, as well as the magnitude of the underpricing. Using a sample of 2,481 US IPOs, they find that textual information can effectively complement financial variables in terms of prediction accuracy because models that use both sources of data produce more accurate estimates. In particular, the model with the best performance using only financial variables achieves 67.5% accuracy whereas the best model with both textual and financial data appears a substantial improvement (6.1%). Also, the use of sophisticated machine learning models drives an increase in the predictive accuracy compared to the traditional logistic regression model (2.5%). The authors attribute the findings to the fact that textual information can reduce the ex ante valuation uncertainty of IPO firms. Finally, they create a portfolio of IPOs based on the out-of-sample machine learning predictions, which remarkably achieves 27.90% average returns. Their portfolio achieves extraordinary abnormal returns in various time dimensions (both in the short and long run), achieving up to 30% better yield than the benchmark.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Journal of Financial Data Science |
| Vol/bind | 5 |
| Udgave nummer | 2 |
| Sider (fra-til) | 100-135 |
| ISSN | 2640-3943 |
| DOI | |
| Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
We thank Athanasios Episcopos, Prodromos Malakasiotis, Thanos Verousis, the participants at the 2017 National Conference of the Financial Engineering and Banking Society, and the 2021 International Conference of the Financial Engineering and Banking Society for their valuable comments and suggestions. Apostolos Katsafados acknowledges financial support cofinanced by Greece and the European Union (European Social Fund) through the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the State Scholarships Foundation (IKY). George Leledakis gratefully acknowledges financial support received from the Research Center of the Athens University of Economics and Business (EP-2256-01). All remaining errors and omissions are our own.
Funding Information:
We thank Athanasios Episcopos, Prodromos Malakasiotis, Thanos Verousis, the participants at the 2017 National Conference of the Financial Engineering and Banking Society, and the 2021 International Conference of the Financial Engineering and Banking Society for their valuable com-ments and suggestions. Apostolos Katsafados acknowledges financial support cofinanced by Greece and the European Union (European Social Fund) through the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the State Scholarships Foundation (IKY). George Leledakis gratefully acknowledges financial support received from the Research Center of the Athens University of Economics and Business (EP-2256-01). All remaining errors and omissions are our own.
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