公司購併在現今的企業環境中扮演的角色日趨重要，公司購併通常是企業間用以尋求互補性的一種商業行為，其目的在於維持己身在企業環境中的商業地位並進而拓展環境中的競爭優勢。為了促成成功的公司購併，選擇出適當的購併對象是一項非常重要的因素；但是，在現存的一些相關研究內尚存在著一些限制，例如：在公司購併預測的模式內，並未將科技相關的變數引用其中、並且尚未以購併者的角度為出發點去考量適當的購併對象。為了解決上述的限制條件，我們提出了一個以專利為基礎的公司購併預測技術，其中包含引入了科技和創新變數的指標進行預測，以及從購併者的角度去思考適當的購併對象去進行我們預測模式的建置與學習。在本篇研究的公司購併預測模式中，我們採用了聚合學習演算法和四十三個從專利分析得到的科技變數去進行學習。除此之外，我們分別收集了台灣及日本公司購併案的資料集合，來評估所提出的公司購併預測技術之效能。實證研究結果本研究所提出的以專利為基礎的公司購併預測技術可以得到不錯的預測效能。

M&A plays an increasingly important role in the present business environment. Companies usually conduct M&A to pursue complementarity from other companies for preserving and extending their competitive advantages. A central and critical factor of the success of M&A is the appropriate selection of one or some target(s). However, existing studies reveal some limitations, such as the absence of technological variable and the exclusion of the characteristics of the acquirer in learning the M&A prediction model. In response to these limitations, we propose an M&A prediction technique which not only encompasses technological and innovative variables as prediction indictors but also takes both acquirer and candidate target into consideration when building an M&A prediction model. Ensemble learning algorithm and forty-three technological variables derived from patent analysis are applied to learn the M&A prediction model. Moreover, we collect two sets of real-world M&A cases to evaluate the proposed patent-analysis-based M&A prediction technique. The evaluation results are encouraging and will serve as a basis for future studies.

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