Skip to main content
SpringerLink
Log in

The Application of Data Analytics for Understanding Patterns of Mergers and Acquisitions and CEO Characteristics in and between Crisis Times

  • Conference paper
  • First Online:
Computer Science and Education in Computer Science (CSECS 2023)

  • 317 Accesses

Abstract

We examine patterns and dynamics of M&A occurrence with attention to three crises or instances of economic disruption—the dot.com bubble, the global financial crisis, and, for future evaluation, the covid-19 global pandemic—while also taking potential CEO demographic (e.g., age) and outcome (e.g., compensation) factors into consideration. Specifically, we examine the frequency, size, and characteristics of M&A transactions across different industries, and we analyze the age, gender, education, experience, and compensation of CEOs across various industries. Drawing on the SDC Platinum and BoardEx databases, we examine US M&A valued over 250 million USD in the 11 global industry classification standard (GICS) sectors from 1999–2018 for n = 14,405 M&A and n = 20,745 CEOs. Using clustering analysis, we define groupings of patterns of M&A transactions before and after the global financial crisis (circa 2008–2009) to show that M&A decreased and then resumed across sectors, although to different degrees for each sector. Using both clustering and PCA, we determine groupings of CEO characteristics before and after the global financial crisis, demonstrating variation in selected demographics of individuals leading organizations before and after that crisis. In addition, initial analysis of our CEO-level data indicates merging to be a predominantly male CEO-driven activity with inferred egoistic, reputational, and power rewards as well as longer-term wealth-building effects, as direct non-contingent compensation does not markedly change for CEOs in the aggregate before and after M&A.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahern, K.R., Daminelli, D., Fracassi, C.: Lost in translation? The effect of cultural values on mergers around the world. J. Financ. Econ. 117(1), 165–189 (2015). https://doi.org/10.1016/j.jfineco.2012.08.006

    Article  Google Scholar 

  2. Arsini, L., Straccamore, M., Zaccaria, A.: Prediction and visualization of mergers and acquisitions using economic complexity. PLoS ONE 18(4), e0283217 (2023). https://doi.org/10.1371/journal.pone.0283217

    Article  Google Scholar 

  3. Balogh, A., Creedy, U., Wright, D.: Time to acquire: regulatory burden and M&A activity. Int. Rev. Financ. Anal. 82, 102047 (2022). https://doi.org/10.1016/j.irfa.2022.102047

    Article  Google Scholar 

  4. Bebchuck, L.A.: The myth of the shareholder franchise. Va. Law Rev. 93(3), 675–732 (2007)

    Google Scholar 

  5. Bhuyan, M.N.H., Subedi, M., Akter, M.: CEO-friendly boards and seasoned equity offerings. J. Behav. Exp. Finan. 36, 100761 (2022). https://doi.org/10.1016/j.jbef.2022.100761

    Article  Google Scholar 

  6. Brueller, N.N., Carmeli, A., Markman, G.D.: Linking merger and acquisition strategies to postmerger integration: a configurational perspective of human resource management. J. Manag. 44(5), 1793–1818 (2018). https://doi.org/10.1177/0149206315626270

    Article  Google Scholar 

  7. Croci, E., et al.: The role of corporate political strategies in M&As. J. Corp. Finan. 43, 260–287 (2017). https://doi.org/10.1016/j.jcorpfin.2017.01.009

    Article  Google Scholar 

  8. Cumming, D., et al.: Mergers and acquisitions research in finance and accounting: past, present, and future. European Financial Management. In Press, pp. 1– 41 (2023). https://doi.org/10.1111/eufm.12417

  9. Ding, C., He, X.: K-means clustering via principal component analysis. In: Proceedings of the 21st International Conference on Machine Learning. Association for Computing Machinery, Banff, Alberta, Canada, p. 29 (2004)

    Google Scholar 

  10. Elnahas, A.M., Kim, D.: CEO political ideology and mergers and acquisitions decisions. J. Corp. Finan. 45, 162–175 (2017). https://doi.org/10.1016/j.jcorpfin.2017.04.013

    Article  Google Scholar 

  11. Ferreira, M.P., dos Reis, N.R., Pinto, C.F.: Three decades of strategic management research on M&As: citations, co-citations, and topics. Glob. Econ. Manage. Rev. 21(1), 13–24 (2016). https://doi.org/10.1016/j.gemrev.2015.12.002

    Article  Google Scholar 

  12. Fralich, R., Bitektine, A.: “Invincibles” and “invisibles”: CEO status and the ‘Matthew effect’ in strategic decision-making. Long Range Plan. 53(3), 101887 (2020). https://doi.org/10.1016/j.lrp.2019.05.007

    Article  Google Scholar 

  13. Guidi, M., et al.: Spreading the sin: an empirical assessment from corporate takeovers. Int. Rev. Financ. Anal. 71, 101535 (2020). https://doi.org/10.1016/j.irfa.2020.101535

    Article  Google Scholar 

  14. Holm, H.J., Nee, V., Opper, S.: Strategic decisions: behavioral differences between CEOs and others. Exp. Econ. 23(1), 154–180 (2020). https://doi.org/10.1007/s10683-019-09604-3

    Article  Google Scholar 

  15. Hussain, T., et al.: Powerful bidders and value creation in M&As. Int. Rev. Financ. Anal. 81, 102076 (2022). https://doi.org/10.1016/j.irfa.2022.102076

    Article  Google Scholar 

  16. Jiang, T.: Using machine learning to analyze merger activity [Brief Research Report]. Front. Appl. Math. Statist. 7, 649501 (2021). https://doi.org/10.3389/fams.2021.649501

    Article  Google Scholar 

  17. Kanungo, T., et al.: A local search approximation algorithm for k-means clustering. Comput. Geom. 28(2), 89–112 (2004). https://doi.org/10.1016/j.comgeo.2004.03.003

    Article  MathSciNet  MATH  Google Scholar 

  18. Leung, H., Tse, J., Westerholm, P.J.: CEO traders and corporate acquisitions. J. Corp. Finan. 54, 107–127 (2019). https://doi.org/10.1016/j.jcorpfin.2017.09.013

    Article  Google Scholar 

  19. Markoczy, L., Kolev, K.D., Qian, C.: Trade-off among stakeholders: CEO political orientation and corporate social irresponsibility. Long Range Plan. 56(2), 102273 (2023). https://doi.org/10.1016/j.lrp.2022.102273

    Article  Google Scholar 

  20. Moeller, S.B., Schlingemann, F.P., Stulz, R.M.: Wealth destruction on a massive scale? A study of acquiring-firm returns in the recent merger wave. J. Finan. 60(2), 757–782 (2005). https://doi.org/10.1111/j.1540-6261.2005.00745.x

    Article  Google Scholar 

  21. Ott, C.: The risks of mergers and acquisitions—analyzing the incentives for risk reporting in item 1A of 10-K filings. J. Bus. Res. 106, 158–181 (2020). https://doi.org/10.1016/j.jbusres.2019.08.028

    Article  Google Scholar 

  22. Petridis, K., et al.: A Support Vector Machine model for classification of efficiency: an application to M&A. Res. Int. Bus. Financ. 61, 101633 (2022). https://doi.org/10.1016/j.ribaf.2022.101633

    Article  Google Scholar 

  23. Plaksina, Y., Gallagher, L., Dowling, M.: CEO social status and M&A decision making. Int. Rev. Financ. Anal. 64, 282–300 (2019). https://doi.org/10.1016/j.irfa.2019.06.006

    Article  Google Scholar 

  24. Seth, S.: The 5 biggest acquisitions in history: deals worth over $100 billion each. Investopedia (2020). https://www.investopedia.com/investing/biggest-acquisitions-in-history/

  25. Simpson, J.J., Sariol, M.: Uncertainty, entrepreneurial orientation, and the pursuit of M&A: managing the unpredictable. J. Bus. Res. 142, 423–434 (2022). https://doi.org/10.1016/j.jbusres.2022.01.006

    Article  Google Scholar 

  26. Wulf, J.: Do CEOs in mergers trade power for premium? Evidence from ‘mergers of equals.’ J. Law Econ. Organ. 20(1), 60–101 (2004)

    Article  Google Scholar 

  27. Zhang, Z., et al.: Improved approximation algorithms for solving the squared metric k-facility location problem. Theoret. Comput. Sci. 942, 107–122 (2023). https://doi.org/10.1016/j.tcs.2022.11.027

    Article  MathSciNet  MATH  Google Scholar 

  28. Zhou, L., et al.: Celebrity CEOs and corporate investment: a psychological contract perspective. Int. Rev. Financ. Anal. 87, 102636 (2023). https://doi.org/10.1016/j.irfa.2023.102636

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Kathleen Berger of the Pardee Library Information Services, Frederick S. Pardee Management Library, Boston University and James Zeitler of the Baker Library Research Services, Harvard Business School, Harvard University, for their respective assistance with the data collection. We deeply thank the Office of the Dean, the Department of Administrative Sciences, and the Department of Computer Science at Boston University Metropolitan College for their support.

Author information

Authors and Affiliations

  1. Administrative Sciences Department, Metropolitan College, Boston University, Boston, MA, 02215, USA

    Kathleen Park & Yue Ying

  2. Computer Science Department, Metropolitan College, Boston University, Boston, MA, 02215, USA

    Eugene Pinsky, Noor Kaiser & Akhil Subramani

Authors
  1. Kathleen Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eugene Pinsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Noor Kaiser
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akhil Subramani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yue Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kathleen Park .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Tanya Zlateva

  2. New Bulgarian University, Sofia, Bulgaria

    Georgi Tuparov

Appendix A: M&A Frequency by GISC Sector Before and After 2008

Appendix A: M&A Frequency by GISC Sector Before and After 2008

Here are comparisons of percentile groupings by quartiles of M&A frequency and value for various GISC sectors before and after 2008:

figure a
figure b

Rights and permissions

Reprints and permissions

Copyright information

© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Park, K., Pinsky, E., Kaiser, N., Subramani, A., Ying, Y. (2023). The Application of Data Analytics for Understanding Patterns of Mergers and Acquisitions and CEO Characteristics in and between Crisis Times. In: Zlateva, T., Tuparov, G. (eds) Computer Science and Education in Computer Science. CSECS 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-44668-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-44668-9_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44667-2

  • Online ISBN: 978-3-031-44668-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation