Title page for etd-0624118-160151


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URN etd-0624118-160151
Author Yu-Lin Chen
Author's Email Address No Public.
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Department Information Management
Year 2017
Semester 2
Degree Master
Type of Document
Language English
Title Singular-Space-Based Support model for M&A Prediction
Date of Defense 2018-07-27
Page Count 75
Keyword
  • Cross verification
  • SVD
  • Decision model
  • Singular Space
  • M&A
  • Business environmental analysis
  • Text mining
  • M&A strategy
  • Abstract Since 1980, in the global competitive environment, M&A has gradually been valued by enterprises. M&A is one of the strategies used to increase the core competence of enterprises. M&A activities are like snowball development. However, data shows that M&A cases have risen year by year, but the failure is as high as 70% to 90%.
    The reasons for the failure of M&A may exist in the internal and external environment of the enterprise. Many researches pointed out that to increase the success rate of M&A, it is necessary to choose the target that is consistent with its own company strategy. Research indicates that past mergers and acquisitions are too dependent on financial variables while ignoring other non-financial variables (i.e. culture, technology, environment) which led to financial variables that determine most of the mergers and acquisitions. In view of this, this paper defines the business environmental variables. When we add the business environmental variables to the financial variables, we calculate the scores of real M&A cases. We hope that by adding non-financial variables, we can improve the score of the forecast target and demonstrate the impact of non-financial variables on M&A target selection.
    In order to reduce the high failure rate of mergers and acquisitions, we use SVD to help us remove the noise value and reduce the dimension. The results show that the method affects the results of the merger. The study analyzed the cases of mergers and acquisitions in Taiwan from 2014 to 2016 and verified them through leave-one-out cross verification. And when SVD is helpful for mergers and acquisitions, we try to explain the factors that are more important for mergers and acquisitions in the singular space.
    We analyzed and summarized the managerial implications of this experiment. Finally, we identify the more important factors for M&A, which means that the relationship between these factors and factors in M&A decisions should be taken seriously.
    Advisory Committee
  • Lin Keng-Pei - chair
  • Hsu Ming-Fu - co-chair
  • Chang Te-Min - advisor
  • Files
  • etd-0624118-160151.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2018-07-24

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