近年來企業併購的熱潮逐漸全球化，企業為了不同的戰略目標，紛紛尋求併購，企業併購活動如滾雪球一般蓬勃發展。根據資料顯示每年有大量的併購案發生，但併購的失敗率卻高達70~90%。
併購成功或失敗可能有各種內部和外部的原因。然而，增加併購成功率最重要的關鍵因素就是選擇符合自己策略的目標公司，不同的併購策略應該有著不同衡量目標的方式。在過去的文獻中，我們發現大多數都只採用財務觀點來衡量併購目標，導致效果有限。有鑑於此，本文提出了一個全新的框架，以水平式併購為例，透過文字探勘來分析公司的年報及新聞等文字資料，計算出公司間文化配適度(Culture fit)及競爭網路的中心性(Centrality)兩個不同觀點的相關指標，將這些非財務指標結合財務指標來評估併購目標，驗證非財務指標對於併購目標選擇的影響。
由於過去企業併購預測的相關文獻，多半採用單一種分類演算法，因此本研究使用不同的分類方法來進行企業併購目標選擇的預測，分別使用邏輯迴歸(Logistic Regression: LR)、支持向量機(Support Vector Machine: SVM)以及隨機森林(Random Forest: RM)三種方法，來分析2014年至2016年間，臺灣本地上市上櫃電子工業之併購事件，並且透過混淆矩陣來比較各分類器的預測效力，以幫助決策者進行併購的目標選擇。
實證結果證實了非財務指標在目標選擇的衡量上有其影響力，透過財務與非財務指標的結合其預測效果都優於單純僅考慮財務指標之模型。此外，透過本研究也可以發現財務指標的影響力在預測模型中還是比較顯著，這說明了非財務指標可以扮演一個輔助的角色。在未來研究的延伸上，更能進一步考量更多不同的觀點及併購決策類型，建構出一個完整的預測模型輔助併購者的目標選擇。

The trends of mergers and acquisitions (M&A) have been globalized recently. To meet different strategical objectives, companies seek for mergers and acquisitions. According to relevant data suggested, there are numerous of M&A taken place every year, however, the failure rate is relatively high, 70%-90%.
There are some possible factors, both internally and externally, leading M&A to success or failure. However, the key to increasing the possibility of being successful in M&A is to select the target company fitting the same strategic objectives. Different strategic of M&A should come up with the measurement of target respectively. According to the literature review, it turns out that most of the M&As were based on financial point of view to measure the target, leading to the limited outcome. Therefore, this article suggests a new point of view. Applying the horizontal merger as the example, calculate the culture fit between cooperation and centrality, two distinct indicators, through text mining, analyzing the annual report and related text data. Combining these textual indicators with financial indicators, to verify the impacts of target selection from overall points of view.
As the result of related prediction literature of M&A, most research only adopt one classification algorithm to predict the target. Thus, this research will implement the variety of methods to predict the targets of M&A. The methods will be Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF). By employing the above methods, this research will focus on the listed and OTC company of electronic industry in Taiwan during the year 2014 to 2016. Furthermore, through confusion matrix to compare the results from different classifiers, to assist decision makers to select the target of M&A.
The proved outcome verifies that there will be a certain level of influences from non-financial indicators on the target selection of M&A. The performance of predicting the outcome by combining both textual and financial indicators will be higher than adopting only financial indicators. However, the results stated that the impacts from financial indicators in the model of prediction still is significance. This is the gesture that non-financial indicators could play the supporting role. In future research, it could put more perspectives and types of M&A decision making into consideration. By doing so, it could build a complete prediction model to assist mergers in selecting their targets.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
TABLE OF CONTENTS vi
LIST OF FIGURES viii
LIST OF TABLES ix
CHAPTER 1 Introduction 1
1.1 Overview 1
1.2 Objective of the research 4
1.3 Organization of the research 5
CHAPTER 2 Literature Review 6
2.1 The type of M&A 6
2.2 Financial Variable 8
2.3 Textual Variable 13
2.3.1 Competition Network Centrality 15
2.3.2 Culture Fit 18
2.4 Classification Algorithm 20
CHAPTER 3 Research Procedure 22
3.1 Pre-merger Process 22
3.2 Proposed Approach 23
CHAPTER 4 Experiments and Results 32
4.1 Experiment Design 32
4.2 Experiment I 39
4.3 Experiment II 41
4.4 Experiment III 42
CHAPTER 5 Conclusion 46
5.1 Summary 46
5.2 Future work 47
REFERENCE 48
APPENDIX 59

Ali-Yrkkö, J., Hyytinen, A., & Pajarinen, M. (2005). Does patenting increase the probability of being acquired? Evidence from cross-border and domestic acquisitions. Applied Financial Economics, 15(14), 1007–1017.
An, S., He, Y., Zhao, Z., & Sun, J. (2006). Measurement of merger and acquisition performance based on artificial neural network. In Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on (Vol. 1, pp. 502–506). IEEE. Retrieved from http://ieeexplore.ieee.org/abstract/document/4216454/
Bao, S., Li, R., Yu, Y., & Cao, Y. (2008). Competitor Mining with the Web. IEEE Transactions on Knowledge and Data Engineering, 20(10), 1297–1310. https://doi.org/10.1109/TKDE.2008.98
Barbara Lind, & John Stevens. (2004). Match your merger integration strategy and leadership style to your merger type. Strategy & Leadership, 32(4), 10–16. https://doi.org/10.1108/10878570410547652
Barnes, P. (2000). The identification of UK takeover targets using published historical cost accounting data Some empirical evidence comparing logit with linear discriminant analysis and raw financial ratios with industry-relative ratios. International Review of Financial Analysis, 9(2), 147–162.
Bernstein, A., Clearwater, S., Hill, S., Perlich, C., & Provost, F. (2002). Discovering knowledge from relational data extracted from business news. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1282999
Bernstein, A., Clearwater, S., & Provost, F. (2003). The relational vector-space model and industry classification. In Proceedings of the Learning Statistical Models from Relational Data Workshop at the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI). Retrieved from https://www.researchgate.net/profile/Scott_Clearwater/publication/228746131_The_relational_vector-space_model_and_industry_classification/links/0deec519e82f591d96000000.pdf
Bertolotti, F., Mattarelli, E., Vignoli, M., & Macrì, D. M. (2015). Exploring the relationship between multiple team membership and team performance: The role of social networks and collaborative technology. Research Policy, 44(4), 911–924.
Brass, D. J., Butterfield, K. D., & Skaggs, B. C. (1998). Relationships and unethical behavior: A social network perspective. Academy of Management Review, 23(1), 14–31.
Brock, D. M. (2005). Multinational acquisition integration: the role of national culture in creating synergies. International Business Review, 14(3), 269–288.
Cabral, I., Grilo, A., Gonçalves-Coelho, A., & Mourão, A. (2016). An agent-based model for analyzing the impact of business interoperability on the performance of cooperativeindustrial networks. Data & Knowledge Engineering, 105, 107–129. https://doi.org/10.1016/j.datak.2015.08.001
Calipha, R., Tarba, S., & Brock, D. (2010). Mergers and acquisitions: a review of phases, motives, and success factors. In Advances in mergers and acquisitions (pp. 1–24). Emerald Group Publishing Limited. Retrieved from http://www.emeraldinsight.com/doi/pdf/10.1108/S1479-361X(2010)0000009004
Christensen, C. M., Alton, R., Rising, C., & Waldeck, A. (2011). The big idea: The new M&A playbook. Harvard Business Review, 89(3), 48–57.
DePamphilis, D. M. (2010). Mergers, Acquisitions, and Other Restructuring Activities: An Integrated Approach to Process, Tools, Cases, and Solutions. Elsevier.
Dietrich, J. K., & Sorensen, E. (1984). An application of logit analysis to prediction of merger targets. Journal of Business Research, 12(3), 393–402.
Fan, A., & Palaniswami, M. (2000). Selecting bankruptcy predictors using a support vector machine approach. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Vol. 6, pp. 354–359). IEEE. Retrieved from http://ieeexplore.ieee.org/abstract/document/859421/
Feenstra, R. C. (2015). Advanced International Trade: Theory and Evidence. Princeton University Press.
Fiordelisi, F., Li, K., Stentella-Lopes, S., & Ricci, O. (2016). Corporate culture and mergers and acquisitions. Unpublished Working Paper.
Fiordelisi, F., & Ricci, O. (2014). Corporate culture and CEO turnover. Journal of Corporate Finance, 28, 66–82.
Freeman, L. C., Roeder, D., & Mulholland, R. R. (1979). Centrality in social networks: II. Experimental results. Social Networks, 2(2), 119–141.
George Lodorfos, & Agyenim Boateng. (2006). The role of culture in the merger and acquisition process: Evidence from the European chemical industry. Management Decision, 44(10), 1405–1421. https://doi.org/10.1108/00251740610715722
Guercini, S., & Ranfagni, S. (2016). Conviviality behavior in entrepreneurial communities and business networks. Journal of Business Research, 69(2), 770–776.
Gugler, K., & Konrad, K. A. (2002). Merger target selection and financial structure. University of Vienna and Wissenschaftszentrum Berlin (WZB). Retrieved from https://www.wu.ac.at/fileadmin/wu/d/i/iqv/Gugler/rio.pdf
Hoberg, G., & Phillips, G. (2010). Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis. The Review of Financial Studies, 23(10), 3773–3811. https://doi.org/10.1093/rfs/hhq053
Hogenboom, F., Frasincar, F., Kaymak, U., & De Jong, F. (2011). An overview of event extraction from text. In Workshop on Detection, Representation, and Exploitation of Events in the Semantic Web (DeRiVE 2011) at Tenth International Semantic Web Conference (ISWC 2011) (Vol. 779, pp. 48–57). Retrieved from https://pdfs.semanticscholar.org/3dcd/fc3d743947d19c76145e12fb5197907e24b7.pdf#page=58
Huang, L., Guo, Y., Zhao, Y., Wang, Y., & Porter, A. L. (2013). The role of text mining of patent in Mergers and Acquisitions. In Technology Management in the IT-Driven Services (PICMET), 2013 Proceedings of PICMET’13: (pp. 2271–2282). IEEE. Retrieved from http://ieeexplore.ieee.org/abstract/document/6641687/
Hunger, J. D., & Wheelen, T. L. (2001). Strategic Management. 1996. Fifth Editions. Addison-Wesley Publishing Company, Inc. Agung J.(penterjemah).
James, A. D., Georghiou, L., & Metcalfe, J. S. (1998). Integrating technology into merger and acquisition decision making. Technovation, 18(8–9), 563590–573591.
Keyvanshokooh, E., Ryan, S. M., & Kabir, E. (2016). Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. European Journal of Operational Research, 249(1), 76–92.
Kim, W. G., & Arbel, A. (1998). Predicting merger targets of hospitality firms (a Logit model). International Journal of Hospitality Management, 17(3), 303–318.
Lau, R. Y. K., Liao, S. S. Y., Wong, K. F., & Chiu, D. K. W. (2012). Web 2.0 Environmental Scanning and Adaptive Decision Support for Business Mergers and Acquisitions. MIS Quarterly, 36(4), 1239-A6.
Liu, Y., Liu, T., & Li, Y. (2014). How to inhibit a partner’s strong and weak forms of opportunism: Impacts of network embeddedness and bilateral TSIs. Industrial Marketing Management, 43(2), 280–292.
Ma, B., Wang, H., Dsouza, M., Lou, J., He, Y., Dai, Z., … Gilbert, J. A. (2016). Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. The ISME Journal, 10(8), 1891.
M&A Statistics - Worldwide, Regions, Industries & Countries. (2016, March 16). Retrieved from https://imaa-institute.org/mergers-and-acquisitions-statistics/
Ma, T., Zhang, Y., Huang, L., Shang, L., Wang, K., Yu, H., & Zhu, D. (2017). Text mining to gain technical intelligence for acquired target selection: A case study for China’s computer numerical control machine tools industry. Technological Forecasting and Social Change, 116, 162–180. https://doi.org/10.1016/j.techfore.2016.10.061
Ma, Z., Pant, G., & Sheng, O. R. (2011). Mining competitor relationships from online news: A network-based approach. Electronic Commerce Research and Applications, 10(4), 418–427.
Ma, Z., Sheng, O. R., & Pant, G. (2009). Discovering company revenue relations from news: A network approach. Decision Support Systems, 47(4), 408–414.
Min, J. H., & Lee, Y.-C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614.
Miner, G. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Academic Press.
Packard, G., Aribarg, A., Eliashberg, J., & Foutz, N. Z. (2016). The role of network embeddedness in film success. International Journal of Research in Marketing, 33(2), 328–342.
Park, H., Yoon, J., & Kim, K. (2013). Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining. Scientometrics, 97(3), 883–909.
Pasiouras, F., & Gaganis, C. (2007). Financial characteristics of banks involved in acquisitions: evidence from Asia. Applied Financial Economics, 17(4), 329–341.
Pasiouras, F., & Tanna, S. (2010). The prediction of bank acquisition targets with discriminant and logit analyses: Methodological issues and empirical evidence. Research in International Business and Finance, 24(1), 39–61.
Pepall, L., Richards, D., & Norman, G. (2008). Industrial organization. Contempory Theory and Empirical Applications, 4. Retrieved from http://new.oberlin.edu/dotAsset/95922.pdf
Preuss, B. (2017). The Application of Text Mining in Business Research. Journal of Accounting & Marketing, 6(2). https://doi.org/10.4172/2168-9601.1000232
Quinn, R., Cameron, K., Degraff, J., & Thakor, A. (2006). Competing values leadership: creating value in organizations. Northhampton, MA: Edward Elgar Publishing Limited.
Quinn, R. E., & Rohrbaugh, J. (1983). A spatial model of effectiveness criteria: Towards a competing values approach to organizational analysis. Management Science, 29(3), 363–377.
Reed, S. F., Lajoux, A. R., & Nesvold, H. P. (2007). The Art of M&A, Fourth Edition: A Merger Acquisition Buyout Guide. McGraw Hill Professional.
Shi, Z., Lee, G. M., & Whinston, A. B. (2014). Towards a better measure of business proximity: topic modeling for analyzing M As. In EC (p. 565). Retrieved from https://pdfs.semanticscholar.org/e52e/1c3b9ce378c4d0bef8e775843f01713419af.pdf
Shi, Z., Lee, G. M., & Whinston, A. B. (2015). Toward a better measure of business proximity: Topic modeling for industry intelligence. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2676630
Song, X. L., Zhang, Q. S., Song, X. L., Chu, Y. H., & Song, E. Z. (2009). A Study on Financial Strategy for Determining the Target Enterprise of Merger and Acquisition. In 2009 IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics (pp. 477–480). https://doi.org/10.1109/SOLI.2009.5203980
Sorensen, D. E. (2000). Characteristics of merging firms. Journal of Economics and Business, 52(5), 423–433. https://doi.org/10.1016/S0148-6195(00)00028-X
Stahl, G. K., & Voigt, A. (2008). Do cultural differences matter in mergers and acquisitions? A tentative model and examination. Organization Science, 19(1), 160–176.
Tang, L., Jing, K., He, J., & Stanley, H. E. (2016). Complex interdependent supply chain networks: Cascading failure and robustness. Physica A: Statistical Mechanics and Its Applications, 443, 58–69.
Teerikangas, S., & Very, P. (2006). The culture–performance relationship in M&A: From yes/no to how. British Journal of Management, 17(S1). Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8551.2006.00477.x/full
Vuong, Q. H., Napier, N. K., & Samson, D. E. (2013). Innovation as determining factor of post-M&A performance: The case of Vietnam. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2313056
Walter, G. A., & Barney, J. B. (1990). Research notes and communications management objectives in mergers and acquisitions. Strategic Management Journal, 11(1), 79–86.
Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.
Wei, C.-P., Jiang, Y.-S., & Yang, C.-S. (2009). Patent analysis for supporting merger and acquisition (m&a) prediction: A data mining approach. Designing E-Business Systems. Markets, Services, and Networks, 187–200.
Wiley: Practitioner’s Complete Guide to M&As: An All-Inclusive Reference, with Website - David T. Emott. (n.d.). Retrieved from http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470920440.html