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

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

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.

Suggested Citation

  • Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Emmanouel & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2019. "Using textual analysis to identify merger participants: Evidence from the U.S. banking industry," MPRA Paper 96893, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:96893
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    References listed on IDEAS

    as
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    14. Pasiouras, Fotios & Tanna, Sailesh & Zopounidis, Constantin, 2007. "The identification of acquisition targets in the EU banking industry: An application of multicriteria approaches," International Review of Financial Analysis, Elsevier, vol. 16(3), pages 262-281.
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    Cited by:

    1. Katsafados, Apostolos & Anastasiou, Dimitris, 2022. "Short-term Prediction of Bank Deposit Flows: Do Textual Features matter?," MPRA Paper 111418, University Library of Munich, Germany.
    2. Anastasiou, Dimitrios & Katsafados, Apostolos G., 2020. "Bank Deposits Flows and Textual Sentiment: When an ECB President's speech is not just a speech," MPRA Paper 99729, University Library of Munich, Germany.
    3. Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2024. "Machine learning in bank merger prediction: A text-based approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 783-797.
    4. Dimitris Anastasiou & Apostolos Katsafados, 2023. "Bank deposits and textual sentiment: When an European Central Bank president's speech is not just a speech," Manchester School, University of Manchester, vol. 91(1), pages 55-87, January.
    5. Chiaramonte, Laura & Dreassi, Alberto & Piserà, Stefano & Khan, Ashraf, 2023. "Mergers and acquisitions in the financial industry: A bibliometric review and future research directions," Research in International Business and Finance, Elsevier, vol. 64(C).
    6. Toan Luu Duc Huynh, 2023. "When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 639-661, August.

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    More about this item

    Keywords

    Textual analysis; text sentiment; bank mergers and acquisitions; acquisition likelihood;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance

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