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  • 學位論文

運用自組織映射法探究台灣半導體公司併購之可能性

Using Self-Organizing Maps on Exploring M&A Possibility of Taiwanese Semiconductor Company.

指導教授 : 施人英
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摘要


近年全球半導體產業面鄰整併潮,大中華地區也不例外,許多中國半導體業者以購併、入股等方式以快速取得成功,也開始鎖定台灣廠商,而併購探討之議題,從單一公司狀況到產業的變動皆須考量其中,若能透過預測與分析,事先準備日後可能面臨的併購或入股事宜,相信在日後若遇到相似情況時,公司能以更充分的準備面對此行為。 完善的併購策略能增強併購雙方公司之綜效,而併購可能性的預測能為公司決策者提供早期評估與決策的參考。本研究之目的,在於利用自我組織映射法(Self-Organizing Map , SOM),分析台灣半導體上市櫃公司的財務、經營、公司狀況等變數資料,分析出可能成為併購標的公司,為企業主決策時提供參考資料,首先將曾經成為併購目標的公司進行質化研究,再利用自我組織映射法分析四年內公司資料的量化研究,兩者相互配合以提出併購可能性相關資訊。 研究結論顯示,運用自我組織映射法能有效將半導體公司樣本做分群,找出與併購目標樣本軌跡相似的其他樣本,能將併購機率較高、軌跡類似的樣本快速區分,藉由圖形顯示淺顯易懂,不同產業間因有其產業特性差異,而產生不同群集組合,再配合其他資料輔助,能為決策者在預測時提供參考資料,此分群分析關鍵在於變數取得,針對產業的特性採用所需之變數,能為群集分析提供較高的貢獻值,使群集分析產生更可靠的群集組合,另一方面雖然本研究採用變數大多為財務資料,但分群結果與實際已發生的併購情形相符,顯示財務資料能適度代表公司狀況,是在併購時不可或缺的參考因素。

並列摘要


In recent years, the global semiconductor industry is facing mergers and acquisitions (M&A), no exception for the Greater China region. Many Chinese semiconductor companies acquire technology by M&A. In addition, they start to focus on Taiwanese semiconductor companies. The issue of M&A have to consider the both of companies and industries. Through prediction and analysis, prepare in advance for the situation while facing M&A. Companies can be well prepared when confront this circumstance. The well-prepared M&A strategy could enhance the synergy of both companies. In this study, we distinguish which company has the higher possibility to be merged in the future by using Self-Organizing Map(SOM)algorithm , collecting data from Taiwanese listed companies in semiconductor industry. This study contains two parts, one is qualitative research and the other is quantitative research. The first section is to find the common points between those companies who have negotiated with Chinese semiconductor companies. The second section is using SOM to analyze companies from 2012-2015. The results of the chart can visualize the connection of those companies . Due to different characteristics of the sub-industries, each sub-industry had a different cluster pattern. And those critical clusters can be used as a reference of companies for others company to face the M&A issues in the future. The key point of SOM in this study is to collect the main variances in different sub-industries because based on industry characteristics, each industry will need specific variances. But this study show that there is none single varience are suitable for all the sub-industries. So if we can find the key variences for each sub-industries,we can do the more accurate analization of this study in the future.

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