Abstract
Mergers and acquisitions are pivotal strategies employed by companies to maintain competitiveness, leading to enhanced production efficiency, scale, and market dominance. Due to their significant financial implications, predicting these operations has become a profitable area of study for both scholars and industry professionals. The accurate forecasting of mergers and acquisitions activities is a complex task, demanding advanced statistical tools and generating substantial returns for stakeholders and investors. Existing research in this field has proposed various methods encompassing econometric models, machine learning algorithms, and sentiment analysis. However, the effectiveness and accuracy of these approaches vary considerably, posing challenges for the development of robust and scalable models.
In this paper, we present a novel approach to forecast mergers and acquisitions activities by utilizing social network analysis. By examining temporal changes in social network graphs of the involved entities, potential transactions can be identified prior to public announcements, granting a significant advantage in the forecasting process. To validate our approach, we conduct a case study on three recent acquisitions made by Microsoft, leveraging the social network platform Twitter. Our methodology involves distinguishing employees from random users and subsequently analyzing the evolution of mutual connections over time. The results demonstrate a strong link between engaged firms, with the connections between Microsoft employees and acquired companies ranging from five to twenty times higher than those of baseline companies in the two years preceding the official announcement. These findings underscore the potential of social network analysis in accurately forecasting mergers and acquisitions activities and open avenues for the development of innovative methodologies.
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References
Activision Blizzard, Inc.: Activision homepage (2023). https://www.activision.com/. Accessed 08 Jan 2023
Adelaja, A., Nayga Jr, R., Farooq, Z.: Predicting mergers and acquisitions in the food industry. Agribusiness: Int. J. 15(1), 1–23 (1999)
Ali-Yrkkö, J., Hyytinen, A., Pajarinen, M.: Does patenting increase the probability of being acquired? Evidence from cross-border and domestic acquisitions. Appl. Financ. Econ. 15(14), 1007–1017 (2005)
Archive, T.: Snowflake GitHub repository (2023). https://github.com/twitter-archive/snowflake/tree/snowflake-2010. Accessed 01 Jan 2023
Barnes, P.: The identification of UK takeover targets using published historical cost accounting data some empirical evidence comparing logit with linear discriminant analysis and raw financial ratios with industry-relative ratios. Int. Rev. Financ. Anal. 9(2), 147–162 (2000)
Center, M.N.: Microsoft to acquire Linkedin (2016). https://news.microsoft.com/2016/06/13/microsoft-to-acquire-linkedin/. Accessed 08 Jan 2023
Center, M.N.: Microsoft to acquire GitHub for \$7.5 billion (2018). https://news.microsoft.com/2018/06/04/microsoft-to-acquire-github-for-7-5-billion/. Accessed 08 Jan 2023
Center, M.N.: Microsoft to acquire activision blizzard to bring the joy and community of gaming to everyone, across every device (2022). https://news.microsoft.com/2022/01/18/microsoft-to-acquire-activision-blizzard-to-bring-the-joy-and-community-of-gaming-to-everyone-across-every-device/. Accessed 08 Jan 2023
Cho, S., Hong, H., Ha, B.C.: A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction. Expert Syst. Appl. 37(4), 3482–3488 (2010)
Consortium, S.: SQLite homepage (2023). https://www.sqlite.org/index.html. Accessed 01 Jan 2023
Discord, I.: Discord API reference (2023). https://discord.com/developers/docs/reference#snowflakes. Accessed 01 Jan 2023
Foundation, P.: Python homepage. https://www.python.org/. Accessed 01 Jan 2023
GitHub, I.: GitHub homepage (2023). https://github.com/. Accessed 08 Jan 2023
GmbH, S.: Value of mergers and acquisition (m&a) transactions worldwide from 2000 to 2022 (2023). https://www.statista.com/statistics/267369/volume-of-mergers-and-acquisitions-worldwide/. Accessed 01 Jan 2023
Gugler, K., Konrad, K.A.: Merger target selection and financial structure. University of Vienna and Wissenschaftszentrum Berlin (WZB) (2002)
Hyndman, R.J.: Computing and graphing highest density regions. Am. Stat. 50(2), 120–126 (1996). https://doi.org/10.1080/00031305.1996.10474359
Kuert, W., Mark, B.: Google and Facebook also looked at buying Linkedin. https://www.vox.com/2016/7/1/12085946/google-facebook-salesforce-linkedin-acquisition. Accessed 01 Jan 2023
Li, Y., Shou, J., Treleaven, P., Wang, J.: Graph neural network for merger and acquisition prediction. In: Proceedings of the Second ACM International Conference on AI in Finance, pp. 1–8 (2021)
Meador, A.L., Church, P.H., Rayburn, L.G.: Development of prediction models for horizontal and vertical mergers. J. Financ. Strateg. Decis. 9(1), 11–23 (1996)
Microsoft, I.: Microsoft homepage. https://www.microsoft.com/. Accessed 08 Jan 2023
Olson, D.L., Delen, D., Meng, Y.: Comparative analysis of data mining methods for bankruptcy prediction. Decis. Support Syst. 52(2), 464–473 (2012)
Pasiouras, F., Gaganis, C.: Financial characteristics of banks involved in acquisitions: evidence from Asia. Appl. Financ. Econ. 17(4), 329–341 (2007)
Ragothaman, S., Naik, B., Ramakrishnan, K.: Predicting corporate acquisitions: an application of uncertain reasoning using rule induction. Inf. Syst. Front. 5(4), 401–412 (2003)
Shin, K.S., Lee, T.S., Kim, H.J.: An application of support vector machines in bankruptcy prediction model. Expert Syst. Appl. 28(1), 127–135 (2005)
Slowinski, R., Zopounidis, C., Dimitras, A.: Prediction of company acquisition in Greece by means of the rough set approach. Eur. J. Oper. Res. 100(1), 1–15 (1997)
Song, X.L., Zhang, Q.S., Chu, Y.H., Song, E.Z.: A study on financial strategy for determining the target enterprise of merger and acquisition. In: 2009 IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics, pp. 477–480. IEEE (2009)
Statista: Twitter global mDAU 2022. https://www.statista.com/statistics/970920/monetizable-daily-active-twitter-users-worldwide/. Accessed 01 Jan 2023
Tsagkanos, A., Georgopoulos, A., Siriopoulos, C.: Predicting Greek mergers and acquisitions: a new approach. Int. J. Financ. Serv. Manage. 2(4), 289–303 (2007)
Twitter, I.: Twitter API homepage. https://developer.twitter.com/en/docs/twitter-api. Accessed 01 Jan 2023
Twitter, I.: Twitter homepage (2023). https://twitter.com/. Accessed 01 Jan 2023
Wei, C.-P., Jiang, Y.-S., Yang, C.-S.: Patent analysis for supporting merger and acquisition (M&A) prediction: a data mining approach. In: Weinhardt, C., Luckner, S., Stößer, J. (eds.) WEB 2008. LNBIP, vol. 22, pp. 187–200. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01256-3_16
Xiang, G., Zheng, Z., Wen, M., Hong, J., Rose, C., Liu, C.: A supervised approach to predict company acquisition with factual and topic features using profiles and news articles on TechCrunch. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 6, pp. 607–610 (2012)
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Visintin, A., Conti, M. (2023). Leveraging Social Networks for Mergers and Acquisitions Forecasting. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_12
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DOI: https://doi.org/10.1007/978-981-99-7254-8_12
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