Textual Information and IPO Underpricing: A Machine Learning Approach

Apostolos Katsafados, Ion Androutsopoulos, Ilias Chalkidis, Manos Fergadiotis, George Leledakis and Emmanouil G. Pyrgiotakis

MPRA Paper from University Library of Munich, Germany

Abstract: This study examines the predictive power of textual information from S-1 filings in explaining IPO underpricing. Our empirical approach differs from previous research, as we utilize several machine learning algorithms to predict whether an IPO will be underpriced, or not. We analyze a large sample of 2,481 U.S. IPOs from 1997 to 2016, and we find that textual information can effectively complement traditional financial variables in terms of prediction accuracy. In fact, models that use both textual data and financial variables as inputs have superior performance compared to models using a single type of input. We attribute our findings to the fact that textual information can reduce the ex-ante valuation uncertainty of IPO firms, thus leading to more accurate estimates.

Keywords: Initial public offerings; First-day returns; Machine learning; Natural language processing (search for similar items in EconPapers)
JEL-codes: G02 G14 G30 G32 (search for similar items in EconPapers)
Date: 2020-10-27
New Economics Papers: this item is included in nep-big, nep-cfn, nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:103813

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