Abstract
Predicting merger waves has been a classical yet challenging problem. In this paper, we propose approaches to predict industry merger waves relying on an integrated dataset including financial statements and supply data, as well as more than 60 thousand firm-level mergers and acquisitions records. We utilize 1000-dimension features—including common-used industry characteristics and novel supply network information—for predictions and train classifiers based on different machine learning methods. The experiments demonstrate the usefulness of our prediction approach, as the predicting precision reaches 91% on acquirers and 96% on targets. By further analysis, some patterns are well explained by financial theories, such as the well-known Tobin’s Q measurement. Especially, new influential factors on merger waves are revealed by the empirical analysis on micro-structure network features. To the best of our knowledge, this paper is one of the first attempts to explore merger waves prediction, and our approaches and findings introduce a new viewpoint for this field.
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References
Ahern KR, Harford J (2014) The importance of industry links in merger waves. J Financ 69(2):527–576
Almeida H, Campello M, Cunha I et al (2014) Corporate liquidity management: a conceptual framework and survey. Annu Rev Financ Econ 6(1):135–162
Anagnostopoulos I, Rizeq A (2021) Conventional and neural network target-matching methods dynamics: the information technology mergers and acquisitions market in the usa. Intell Syst Account Financ Manag 28(2):97–118
Bereskin F, Byun SK, Officer MS et al (2018) The effect of cultural similarity on mergers and acquisitions: evidence from corporate social responsibility. J Financ Quant Anal 53(5):1995–2039
Burkart M, Panunzi F et al (2006) Takeovers. CEPR Discussion Papers (5572)
Cai J, Vijh AM (2007) Incentive effects of stock and option holdings of target and acquirer ceos. J Financ 62(4):1891–1933
Cao C, Goldie BA, Liang B et al (2016) What is the nature of hedge fund manager skills? Evidence from the risk-arbitrage strategy. J Financ Quant Anal 51(3):929–957
Dong M, Tremblay A (2021) Does stock misvaluation drive merger waves? Available at SSRN 3224600
Duksaitė E, Tamošiūnienė R (2009) Why companies decide to participate in mergers and acquisition transactions. Mokslas-Lietuvos ateitis/Science-Future of Lithuania 1(3):21–25
Eckbo BE (2014) Corporate takeovers and economic efficiency. Annu Rev Financ Econ 6(1):51–74
Erel I, Jang Y, Weisbach MS (2015) Do acquisitions relieve target firms’ financial constraints? J Financ 70(1):289–328
Galbraith JK (2017) American capitalism: the concept of countervailing power. Routledge, London
Goel AM, Thakor AV (2010) Do envious ceos cause merger waves? Rev Financ Stud 23(2):487–517
Gugler K, Mueller DC, Weichselbaumer M et al (2012) Market optimism and merger waves. Manag Decis Econ 33(3):159–175
Harford J (2005) What drives merger waves? J Financ Econ 77(3):529–560
Huang H, Dong Y, Tang J et al (2018) Will triadic closure strengthen ties in social networks? ACM Trans Knowl Discov Data 12(3):1–25
Katsafados AG, Leledakis GN, Pyrgiotakis EG, et al (2021) Machine learning in us bank merger prediction: a text-based approach. Available at SSRN 3848854
Komlenovic S, Mamun A, Mishra D (2011) Business cycle and aggregate industry mergers. J Econ Financ 35(3):239–259
Lodorfos G, Boateng A (2006) The role of culture in the merger and acquisition process: evidence from the European chemical industry. Manag Decis 44(10):1405–1421
Maksimovic V, Phillips G, Yang L (2013) Private and public merger waves. J Financ 68(5):2177–2217
Malmendier U, Moretti E, Peters FS (2018) Winning by losing: evidence on the long-run effects of mergers. Rev Financ Stud 31(8):3212–3264
Mitchell ML, Mulherin JH (1996) The impact of industry shocks on takeover and restructuring activity. J Financ Econ 41(2):193–229
Moriarty R, Ly H, Lan E et al (2019) Deal or no deal: predicting mergers and acquisitions at scale. In: 2019 IEEE International Conference on Big Data, pp 5552–5558
Ovtchinnikov AV (2013) Merger waves following industry deregulation. J Corp Financ 21:51–76
Rhodes-Kropf M, Robinson DT, Viswanathan S (2005) Valuation waves and merger activity: the empirical evidence. J Financ Econ 77(3):561–603
Routledge BR, Sacchetto S, Smith NA (2017) Predicting merger targets and acquirers from text. Tech Rep
Rozen-Bakher Z (2018) Comparison of merger and acquisition (M&A) success in horizontal, vertical and conglomerate M&As: industry sector vs services sector. Serv Ind J 38(7–8):492–518
Shao B, Asatani K, Sakata I (2018) Categorization of mergers and acquisitions in Japan using corporate databases: a fundamental research for prediction. In: 2018 IEEE International Conference on Industrial Engineering and Engineering Management, pp 1523–1527
Shi X, Wang L, Liu S et al (2017) Investigating microstructure patterns of enterprise network in perspective of ego network. In: Asia–Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, pp 444–459
Shleifer A, Vishny RW (2003) Stock market driven acquisitions. J Financ Econ 70(3):295–311
Tsagkanos A, Georgopoulos A, Siriopoulos C (2007) Predicting Greek mergers and acquisitions: a new approach. Int J Financ Serv Manag 2(4):289–303
Venuti K (2021) Predicting mergers and acquisitions using graph-based deep learning. arXiv preprint arXiv:2104.01757
Wei CP, Jiang YS, Yang CS (2008) Patent analysis for supporting merger and acquisition (M&A) prediction: a data mining approach. In: Workshop on E-Business, vol 22. Springer, pp 187–200
Yang YC, Ke YS, Wu W et al (2019) Recommendation as a service in mergers and acquisitions transactions. In: International Conference on Human-Computer Interaction, Springer, pp 151–159
Zhu Q, Li X, Li F et al (2020) Data-driven approach to find the best partner for merger and acquisitions in banking industry. Indus Manag Data Syst 121(4):879–893
Acknowledgements
The authors would like to acknowledge the support provided by the National Natural Science Foundation of China under Grant 61872222, the Key Research and Development Program of Shandong Province (2020CXGC010102), the project ZR2020LZH011 supported by Shandong Provincial Natural Science Foundation, and the Fundamental Research Funds of Shandong University (11530061340342).
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Qu, Y., Wang, L., Qi, Q. et al. Predict industry merger waves utilizing supply network information. J Ambient Intell Human Comput (2024). https://doi.org/10.1007/s12652-024-04792-0
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DOI: https://doi.org/10.1007/s12652-024-04792-0