Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach

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

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observations during the period 1994-2016. To predict bidders and targets, we use textual information along with financial variables as inputs to several machine learning models. Our key findings suggest that: (1) when textual information is used as a single type of input, the predictive accuracy of our models is similar, or even better, compared to the models using only financial variables as inputs, and (2) when we jointly use textual information and financial variables as inputs, the predictive accuracy of our models is substantially improved compared to models using a single type of input. Therefore, our findings highlight the importance of textual information in a bank merger prediction task.

Keywords: Bank merger prediction; Textual analysis; Natural language processing; Machine learning (search for similar items in EconPapers)
JEL-codes: C38 C45 G1 G2 G21 G3 G34 (search for similar items in EconPapers)
Date: 2021-06-12
New Economics Papers: this item is included in nep-ban, nep-big, nep-cfn, nep-cmp, nep-com and nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Machine learning in bank merger prediction: A text-based approach (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:108272

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