TY - JOUR

T1 - Textual Information and IPO Underpricing

T2 - A Machine Learning Approach

AU - Katsafados, Apostolos G.

AU - Leledakis, George N.

AU - Pyrgiotakis, Emmanouil G.

AU - Androutsopoulos, Ion

AU - Chalkidis, Ilias

AU - Fergadiotis, Manos

N1 - Publisher Copyright: © 2023 With Intelligence LLC.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.3905/jfds.2023.1.121

DO - 10.3905/jfds.2023.1.121

M3 - Journal article

AN - SCOPUS:85152375507

VL - 5

SP - 100

EP - 135

JO - Journal of Financial Data Science

JF - Journal of Financial Data Science

SN - 2640-3943

IS - 2

ER -