Title page for etd-0530117-140725


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URN etd-0530117-140725
Author Yi-syuan Ke
Author's Email Address No Public.
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Department Information Management
Year 2016
Semester 2
Degree Master
Type of Document
Language English
Title Forecast the M&A targets in IT industry using Support Vector Machine
Date of Defense 2017-06-30
Page Count 41
Keyword
  • M&A
  • Logistic Regression
  • Financial data
  • M&A-SVM model
  • SVM
  • Abstract In this era, companies are able to occupy a place in this vast market, but many companies face the change of market. They will lose the original scale of operation, or finally go bankrupt, or resell the company to other many the capable of companies. It calls the mergers or acquisitions of companies (M&A). Recently, many examples, such as once in the search engine page scenery moment of Yahoo, because the company's operating faced policy problems. They missed the opportunity to succeed in this area and Google, Facebook rose. Yahoo gradually lost the original sizes, and finally in 2016 decided to sell to Verizon Communications. Whether a company can run for a long time is needed to change with the times, once missed the point in time, it is possible to let other companies catch up or even beyond. The worst result is the company had been merged in order to survive. We hope to have a good mechanism, so that companies do a good decision in the merger decision. In the era of big data, the data has become a helper, you can get some data through the surface cannot clearly see, and it may help to the company's future development. Financial information for a company's business is very important. So the analysis of financial information will help the company to operate in making the merger decision and it can effectively grasp the opportunity to acquire the company.
    Advisory Committee
  • Lin Keng-Pei - chair
  • Jin Yong - co-chair
  • Yang Yu-Chen - advisor
  • Files
  • etd-0530117-140725.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2017-07-17

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