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Analysing voting behaviour in the United States banking sector through eigenvalue decomposition

Author

Listed:
  • Juan Pineiro-Chousa

    (USC - Universidade de Santiago de Compostela [Spain])

  • Marcos Vizcaíno-González

    (Universidade da Coruña)

  • Jérôme Caby

    (Ecole Supérieure du Commerce Extérieur - ESCE - International business school)

Abstract

Using data about votes emitted by funds in corporate meetings held by US banks from 2003 to 2013, we propose a novel approach based on eigenvalue decomposition to address the issue of communality in voting decisions. Our results indicate that there is a main underlying feature that contributes to explain this voting behaviour. Also, a dimensionality reduction could be accomplished so that a subset of the original data can replicate the sample. These findings confirm that there may be a sort of homogeneous or systematic component when it comes to explain the voting pattern into the banking industry.

Suggested Citation

  • Juan Pineiro-Chousa & Marcos Vizcaíno-González & Jérôme Caby, 2015. "Analysing voting behaviour in the United States banking sector through eigenvalue decomposition," Post-Print hal-02001676, HAL.
    • Handle: RePEc:hal:journl:hal-02001676
    DOI: 10.1080/13504851.2015.1114568
    Note: View the original document on HAL open archive server: https://hal.science/hal-02001676
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    References listed on IDEAS

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    1. Plerou, V & Gopikrishnan, P & Rosenow, B & Amaral, L.A.N & Stanley, H.E, 2000. "A random matrix theory approach to financial cross-correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 374-382.
    2. Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
    3. Bernd Rosenow & Vasiliki Plerou & Parameswaran Gopikrishnan & Luís A. Nunes Amaral & H. Eugene Stanley, 2000. "Application Of Random Matrix Theory To Study Cross-Correlations Of Stock Prices," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 399-403.
    4. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    5. Natasha Burns & Kristina Minnick, 2013. "Does Say-on-Pay Matter? Evidence from Say-on-Pay Proposals in the United States," The Financial Review, Eastern Finance Association, vol. 48(2), pages 233-258, May.
    6. Laurent Laloux & Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Random Matrix Theory And Financial Correlations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 391-397.
    7. Raj Kumar Pan & Sitabhra Sinha, 2007. "Collective behavior of stock price movements in an emerging market," Papers 0704.0773, arXiv.org, revised Nov 2007.
    8. Varsha Kulkarni & Nivedita Deo, 2007. "Correlation and volatility in an Indian stock market: A random matrix approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(1), pages 101-109, November.
    9. R. Rak & S. Drozdz & J. Kwapien & P. Oswiecimka, 2006. "Correlation matrix decomposition of WIG20 intraday fluctuations," Papers physics/0606041, arXiv.org, revised Nov 2006.
    10. Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
    11. Wilcox, Diane & Gebbie, Tim, 2007. "An analysis of cross-correlations in an emerging market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 584-598.
    12. Zdzislaw Burda & Jerzy Jurkiewicz, 2003. "Signal and Noise in Financial Correlation Matrices," Papers cond-mat/0312496, arXiv.org, revised Feb 2004.
    13. Burda, Zdzisław & Jurkiewicz, Jerzy, 2004. "Signal and noise in financial correlation matrices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 67-72.
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    Cited by:

    1. Juan Pineiro-Chousa & Marcos Vizcaíno-González & Jérôme Caby, 2018. "Linking market capitalisation and voting pattern in corporate meetings," Post-Print halshs-02001463, HAL.
    2. López-Cabarcos, M. Ángeles & Vizcaíno-González, Marcos & López-Pérez, M. Luisa, 2023. "Gender diversity on boards: Determinants that underlie the proposals for female directors," Technological Forecasting and Social Change, Elsevier, vol. 190(C).

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    More about this item

    Keywords

    singular value decomposition; eigenvalue decomposition; Voting behaviour; spectral decomposition; spectral decomposition JEL codes: G01; G21; G32; G34 2;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G - Financial Economics

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