Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach
63 Pages Posted: 15 Jun 2021 Last revised: 17 Jun 2021
Date Written: May 18, 2021
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
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