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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)

Abstract

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.

Keywords

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

Notes

Acknowledgement

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.

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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

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