Using textual analysis to identify merger participants: Evidence from the U.S. banking industry

•We use the sentiment of annual reports to gauge the banks’ acquisition likelihood.•Textual analysis is an important tool in identifying bank merger participants.•Higher fraction of positive words translates to higher bidder probability.•Higher fraction of negative words translates to higher target...

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Bibliographic Details
Published inFinance research letters Vol. 42; p. 101949
Main Authors Katsafados, Apostolos G., Androutsopoulos, Ion, Chalkidis, Ilias, Fergadiotis, Emmanouel, Leledakis, George N., Pyrgiotakis, Emmanouil G.
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.10.2021
Subjects
Acquisition likelihood
Bank mergers and acquisitions
Text sentiment
Textual analysis
G40
Acquisition likelihood
G21
Text sentiment
Bank mergers and acquisitions
G34
G14
Textual analysis
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Summary:•We use the sentiment of annual reports to gauge the banks’ acquisition likelihood.•Textual analysis is an important tool in identifying bank merger participants.•Higher fraction of positive words translates to higher bidder probability.•Higher fraction of negative words translates to higher target probability. In this paper, we use the sentiment of annual reports to gauge the likelihood of a bank to participate in a merger transaction. We conduct our analysis on a sample of annual reports of listed U.S. banks over the period 1997 to 2015, using the Loughran and McDonald's lists of positive and negative words for our textual analysis. We find that a higher frequency of positive (negative) words in a bank's annual report relates to a higher probability of becoming a bidder (target). Our results remain robust to the inclusion of bank-specific control variables in our logistic regressions.
ISSN:1544-6123
1544-6131
DOI:10.1016/j.frl.2021.101949