Machine learning in bank merger prediction: A text-based approach

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

European Journal of Operational Research, 2024, vol. 312, issue 2, 783-797

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. We find that when we jointly use textual information and financial variables as inputs, the performance of our models is substantially improved compared to models using a single type of input. Furthermore, we find that the performance improvement due to the inclusion of text is more noticeable in predicting future bidders, a task which is less explored in the relevant literature. Therefore, our findings highlight the importance of textual information in a bank merger prediction task.

Keywords: Finance; Bank merger prediction; Textual analysis; Natural language processing; Machine learning (search for similar items in EconPapers)
JEL-codes: C63 G14 G21 G34 G40 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221723005982
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:312:y:2024:i:2:p:783-797

DOI: 10.1016/j.ejor.2023.07.039

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().