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

63 Pages Posted: 15 Jun 2021 Last revised: 17 Jun 2021

See all articles by Dr. Apostolos G. Katsafados

Dr. Apostolos G. Katsafados

Athens University of Economics and Business - Department of Accounting and Finance

George N. Leledakis

Athens University of Economics and Business, School of Business, Department of Accounting and Finance

Emmanouil G. Pyrgiotakis

University of Essex - Essex Business School

Ion Androutsopoulos

Athens University of Economics and Business

Emmanouel Fergadiotis

Athens University of Economics and Business

Date Written: May 18, 2021

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

JEL Classification: C63, G14, G21, G34, G40

Suggested Citation

Katsafados, Dr. Apostolos G. and Leledakis, George N. and Pyrgiotakis, Emmanouil G. and Androutsopoulos, Ion and Fergadiotis, Emmanouel, Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach (May 18, 2021). Available at SSRN: https://ssrn.com/abstract=3848854 or http://dx.doi.org/10.2139/ssrn.3848854

Dr. Apostolos G. Katsafados

Athens University of Economics and Business - Department of Accounting and Finance ( email )

76 Patission Street
GR-104 34 Athens
Greece

George N. Leledakis (Contact Author)

Athens University of Economics and Business, School of Business, Department of Accounting and Finance ( email )

76 Patission Str.
Athens, 104 34
Greece
+30 210 8203 459 (Phone)
+30 210 8228 816 (Fax)

HOME PAGE: http://www.aueb.gr/en/faculty_page/leledakis-georgios

Emmanouil G. Pyrgiotakis

University of Essex - Essex Business School ( email )

Wivenhoe Park
Colchester, CO4 3SQ
United Kingdom

Ion Androutsopoulos

Athens University of Economics and Business ( email )

76 Patission Street
Athens, 104 34
Greece

Emmanouel Fergadiotis

Athens University of Economics and Business ( email )

76 Patission Street
Athens, 104 34
Greece

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