在全球經濟日益緊密下，併購已成為全球商業領域中的重要趨勢，促使公司進行所有權、資產和經營單位的合併和轉移。在數位轉型和金融科技新創公司崛起的時代，金融服務業迅速成長，更加顯示了有效合作策略的重要性。因此本研究運用機器學習技術，針對金融科技(Fintech)產業中的併購(M&A)交易進行預測，採用財務模型和技術模型的方法，以預測潛在的併購交易對象，並評估公司合併後的績效和創新情況。除了整合財務指標和技術指標外，還引入了文化(cultural fit)、潛在(potential fit)和相似性(similarity fit)等變數，以增強預測能力並深入瞭解影響併購結果的因素。其中，潛在變數包括公司參與活動的數量和文章數量、公司創始人數量、競爭對手數量等；而相似性變數則使用了doc2vec方法計算兩間公司的技術相似程度。研究結果顯示，技術模型優於財務模型，並強調了專利相關指標和相似性變數的重要性，在訓練模型中相似性變數為重要指標，此外，在併購後分析的部分，公司參與的事件數量對合併後的公司績效具有顯著影響。最後，此研究為高階管理人員提供了有價值的併購決策工具，結合了財務和專利數據，以更加了解併購中的關鍵因素，同時提供了進一步研究的方向和建議，以促進學術和實務上對於併購領域的深入瞭解。

This study utilizes machine learning techniques to predict M&A transactions in the fintech industry, adopting a financial and technical model approach to anticipate potential M&A targets and assess post-merger performance and innovation. Besides integrating financial and technical indicators, the study also introduces variables such as cultural fit, potential fit, and similarity fit to enhance predictive capabilities and delve deeper into factors affecting M&A outcomes. The findings reveal the knowledge-based model's superiority over the financial-based one, highlighting the significance of patent-related indicators and similarity variables as key in model training. Moreover, the number of events a company participates in significantly impacts post-merger performance. Ultimately, this study offers valuable decision-making tools for senior management, incorporating financial and patent data to better understand key factors in M&A, providing directions and suggestions for further research, and facilitating a more in-depth understanding of the M&A field both academically and practically.

論文審定書 i
摘要 ii
Abstract iii
CHAPTER 1- Introduction 1
1.1 Background 1
1.2 Purpose 3
CHAPTER 2- Literature Review 6
2.1 Technology M&A 6
2.1.1 Partner Selection 7
2.1.2 Post-Merger Outcomes 8
2.2 Variables for M&A Prediction 10
2.2.1 Cultural Indicators 10
2.2.2 Financial Indicators 12
2.2.3 Knowledge Indicators 14
2.2.4 Potential Indicators 17
CHAPTER 3- Proposed Model 19
3.1 Variables for M&A Prediction 24
3.1.1 Cultural Fit 24
3.1.2 Financial Fit 26
3.1.3 Knowledge Fit 27
3.1.4 Potential Fit 29
3.1.5 Similarity Fit 30
3.2 Merging Partner Matching 35
3.2.1 Training Phase 36
3.2.2 Prediction Phase 40
3.3 Post-Merger 41
3.4 Dependent Variables for Post-Merger 42
CHAPTER 4- Data and Experiment 45
4.1 Data Collection 45
4.2 Preprocessing 45
4.3 Evaluation Design 47
4.4 Experiment Results 48
4.4.1 Merging Partner Matching 49
4.4.2 Post-Merger 56
CHAPTER 5- Conclusion 62
5.1 Conclusion 62
5.2 Limitations and Further Research 63
Reference 66

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