Recommendation as a Service in Mergers and Acquisitions Transactions

  • Yu-Chen YangEmail author
  • Yi-Syuan Ke
  • Weiwei Wu
  • Keng-Pei Lin
  • Yong Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11589)


Mergers and acquisitions (M&A) happens frequently between corporations to combine and/or transfer their ownerships, operating units and assets. The purpose of the study is to develop a service that is able to recommend a feasible M&A deal. We integrate the support vector machine model with the kernel tricks to automatically determine M&A deals. In the end of the study, our proposed technique is empirically validated, and the results show the effectiveness of the recommendation service.


Mergers and acquisitions Machine learning Support vector machine Financial kernel Recommendation service 



This work is supported by the Ministry of Science and Technology of Taiwan (106-2410-H-110-082), and the Intelligent Electronic Commerce Research Center from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.


  1. 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)CrossRefGoogle Scholar
  2. An, S., He, Y., Zhao, Z., Sun, J.: Measurement of merger and acquisition performance based on artificial neural network. In: 2006 5th IEEE International Conference on Cognitive Informatics, pp. 502–506. IEEE, Beijing (2006)Google Scholar
  3. 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)CrossRefGoogle Scholar
  4. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM, Pittsburgh (1992)Google Scholar
  5. Cecchini, M., Aytug, H., Koehler, G.J., Pathak, P.: Detecting management fraud in public companies. Manag. Sci. 56(7), 1146–1160 (2010)CrossRefGoogle Scholar
  6. Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10(5), 1055–1064 (1999)CrossRefGoogle Scholar
  7. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  8. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  9. Cristianini, N., Shawe-Taylor, J., Lodhi, H.: Latent semantic kernels. J. Intell. Inf. Syst. 18(2–3), 127–152 (2002)CrossRefGoogle Scholar
  10. Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., Weingessel, A.: Misc functions of the Department of Statistics (e1071), TU Wien. R package, 1, 5–24 (2008)Google Scholar
  11. Hagedoorn, J., Duysters, G.: The effect of mergers and acquisitions on the technological performance of companies in a high-tech environment. Technol. Anal. Strat. Manag. 14(1), 67–85 (2002)CrossRefGoogle Scholar
  12. Hornik, K., Meyer, D., Karatzoglou, A.: Support vector machines in R. J. Stat. Softw. 15(9), 1–28 (2006)Google Scholar
  13. Hu, X., Pan, Y.: Knowledge Discovery in Bioinformatics: Techniques, Methods, and Applications, vol. 5. Wiley, Hoboken (2007)CrossRefGoogle Scholar
  14. Hua, S., Sun, Z.: A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J. Mol. Biol. 308(2), 397–407 (2001)CrossRefGoogle Scholar
  15. King, D.R., Slotegraaf, R.J., Kesner, I.: Performance implications of firm resource interactions in the acquisition of R&D-intensive firms. Organ. Sci. 19(2), 327–340 (2008)CrossRefGoogle Scholar
  16. Meador, A.L., Church, P.H., Rayburn, L.G.: Development of prediction models for horizontal and vertical mergers. J. Financ. Strat. Decis. 9(1), 11–23 (1996)Google Scholar
  17. Pasiouras, F., Gaganis, C.: Financial characteristics of banks involved in acquisitions: Evidence from Asia. Appl. Financ. Econ. 17(4), 329–341 (2007)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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, SOLI 2009, pp. 477–480. IEEE, Chicago (2009)Google Scholar
  20. Tsagkanos, A., Georgopoulos, A., Siriopoulos, C.: Predicting Greek mergers and acquisitions: a new approach. Int. J. Financ. Serv. Manag. 2(4), 289–303 (2007)Google Scholar
  21. Yang, C.-S., Wei, C.-P., Chiang, Y.-H.: Exploiting technological indicators for effective technology merger and acquisition (M&A) predictions. Decis. Sci. 45(1), 147–174 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yu-Chen Yang
    • 1
    Email author
  • Yi-Syuan Ke
    • 1
  • Weiwei Wu
    • 2
  • Keng-Pei Lin
    • 1
  • Yong Jin
    • 2
  1. 1.National Sun Yat-Sen UniversityKaohsiungTaiwan
  2. 2.The Hong Kong Polytechnic UniversityHung HomHong Kong

Personalised recommendations