在這個時代，各個公司都為了能在這廣大市場佔有一席之地，可是許多公司面臨市場的變動下，失去原有的經營規模，最後走向倒閉，不然就是將公司轉售給其他更有能力的公司經營下去，所謂的公司合併或併購。近來也很多例子，像是曾經在搜尋引擎網頁風光一時的奇摩(Yahoo)，因為公司的經營方針出現問題，錯失能在這方面領域出頭的良機，給了Google、Facebook的崛起，讓它漸漸失去原有的規模，最後在2016年決定轉賣給Verizon Communications。一間公司是否能長久經營是需要隨著時代前進而改變，一旦錯過了那個時間點，就有可能會讓其他公司能夠趕上甚至超越，最壞的結果就是不得已只能和公司合併，保持生存，因此這樣的議題一定會持續下去。但合併的事情是一件相當複雜的東西，需要經過許多的顧慮和分析討論，所以我們希望能夠有良好的機制，讓公司在做合併決策時有好的輔助。大數據的時代來臨，數據成了不可或缺的幫手，可以透過資料分析獲得一些表面上無法清楚看見的事物，而這些數據有可能幫助到公司的未來發展。財務資料對於一間公司的經營相當重要，公司營運狀況的好壞可以從財務上清楚看到。因此分析財務資料有助於公司經營，在做合併決策時能有效率地掌握進場機會收購公司，讓公司順利擴張並強化在市場上的地位。

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.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
Table of contents v
Figure vii
Table viii
1. Introduction 1
2. Literature 7
2.1 M&A 7
2.2 Support Vector Machine (SVM) 12
2.3 The Kernel 14
3. The empirical study 17
3.1 The previously of the experiment 17
3.2 Data collection 17
3.3 The preprocessing of data 18
3.4 The model: M&A-SVM Model 22
4. Experimental results 25
4.1 Distinguishing the data from years 25
4.2 The polynomial kernel of SVM 26
4.3 Two years vs One year 26
4.4 SVM vs. Logistic 27
4.5 The summary of our experiment 28
5. Conclusion 29
5.1 Summary 29
5.2 The future research 30
6. References 31

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