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

Apostolos Katsafados, Ion Androutsopoulos, Ilias Chalkidis, Emmanouel Fergadiotis, George Leledakis and Emmanouil G. Pyrgiotakis

Finance Research Letters, 2021, vol. 42, issue C

Abstract: 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.

Keywords: Textual analysis; Text sentiment; Bank mergers and acquisitions; Acquisition likelihood (search for similar items in EconPapers)
JEL-codes: G14 G21 G34 G40 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Working Paper: Using textual analysis to identify merger participants: Evidence from the U.S. banking industry (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:42:y:2021:i:c:s1544612321000301

DOI: 10.1016/j.frl.2021.101949

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