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

MPRA Paper from University Library of Munich, Germany

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: G00 G17 G21 G34 (search for similar items in EconPapers)
Date: 2019-11
New Economics Papers: this item is included in nep-ban, nep-big, nep-cfn and nep-com
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Using textual analysis to identify merger participants: Evidence from the U.S. banking industry (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:96893

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