基於分層注意力網路之 預測併購成敗

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：16

、訪客IP：3.144.89.197

姓名

洪培紋(PEI-WEN HONG) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

基於分層注意力網路之預測併購成敗
(Predicting the Success or Failure of Mergers and Acquisitions Based on Hierarchical Attention Networks)

相關論文

★ 應用失效模式效應分析於產品研發時程之改善	★ 服務品質因子與客戶滿意度關係研究-以汽車保修廠服務為例
★ 家庭購車決策與行銷策略之研究	★ 計程車車隊派遣作業之研究
★ 電業服務品質與服務失誤之探討-以台電桃園區營業處為例	★ 應用資料探勘探討筆記型電腦異常零件-以A公司為例
★ 車用配件開發及車主購買意願探討(以C公司汽車配件業務為實例)	★ 應用田口式實驗法於先進高強度鋼板阻抗熔接條件最佳化研究
★ 以層級分析法探討評選第三方物流服務要素之研究-以日系在台廠商為例	★ 變動良率下的最佳化批量研究
★ 供應商庫存管理架構下運用層級分析法探討供應商評選之研究-以某電子代工廠為例	★ 台灣地區快速流通消費產品銷售預測模型分析研究–以聯華食品可樂果為例
★ 競爭優勢與顧客滿意度分析以中華汽車為例	★ 綠色採購導入對電子代工廠的影響-以A公司為例
★ 以德菲法及層級分析法探討軌道運輸業之供應商評選研究–以T公司為例	★ 應用模擬系統改善存貨管理制度與服務水準之研究-以電線電纜製造業為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

併購是企業中可以達到永續經營的手段。然而之前很少人探討關於預測併購交易是否成功的研究。因此，如果在公司要進行併購交易之前，能夠有一個預測模型，來預測併購後成功與否。不僅可以讓公司的管理者在併購決策上提供幫助，也可以讓投資者做出更明智的投資決策。
本論文結合文本與數值特徵預測併購成敗。從10-K文件中的管理層討論與分析(MD&A)來提取文本特徵，透過分層注意力網路來建構文本向量，並使用公司每三年的MD&A所計算的MD&A時間變化量，以及結合15項財務指標，來當作模型的輸入因子，使用貝氏神經網路進行訓練。
本研究旨在預測併購成功，然而在實驗結果的表現不如預期，可能來自內部因素或外部因素的影響，包括財務狀況、併購策略等原因，因此只使用財務指標或加入文本數據來進行預測，有可能造成預測結果不準確。應當將影響併購的因素更全面化地考慮，並透過特徵選取，提取只對預測併購成功有影響的特徵進行訓練，並在做出最終決策之前，綜合考慮各個模型的結果、相關領域的專業知識以及市場情況等多方面的信息。

摘要(英)

Mergers and acquisitions are the means by which enterprises can achieve sustainable operation. However, little research has been done on predicting the success of M&A deals before. Therefore, if the company is going to conduct an M&A transaction, it can have a predictive model to predict whether it will be successful after the M&A transaction. Not only can the company′s managers provide assistance in M&A decisions, but it can also allow investors to make more informed investment decisions.
This paper combines text and numerical features to predict the success or failure of mergers and acquisitions. Extract text features from the management discussion and analysis (MD&A) in the 10-K file, construct the text vector through Hierarchical Attention Network, and use the MD&A time change calculated by the company′s MD&A every three years, and combine 15 financial indicators are used as the input factors of the model, and use Bayesian neural network to train model.
This study aims to predict the success of mergers and acquisitions. However, the performance of the experimental results is not as expected, which may come from internal factors or external factors, including financial conditions, merger strategies and other reasons.
Therefore, only using financial indicators or adding text data to forecast may cause inaccurate forecast results. The factors affecting mergers and acquisitions should be considered more comprehensively, and through feature selection, only the features that have an impact on predicting the success of mergers and acquisitions should be extracted for training. And before making a final decision, comprehensively consider the results of various models, professional knowledge in related fields, and market conditions and other information.

關鍵字(中)

★ 併購
★ 深度學習
★ 文本分析
★ 分層注意力網路

關鍵字(英)

★ Mergers and acquisitions
★ Deep learning
★ text analysis
★ Hierarchical Attention Network

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章緒論 1
1.1 研究背景與動機 1
1.2 問題定義 3
1.3 研究目的 3
1.4 研究方法 4
1.5 研究流程與架構 4
第二章文獻探討 5
2.1 併購預測相關研究 5
2.2 經濟增長值(Economic Value Added, EVA) 6
2.3 文本分析 6
2.4 分層注意力網路(Hierarchical Attention Network, HAN) 7
2.4.1 詞嵌入(Word Embedding) 9
2.4.2 雙向長短期記憶(Bidirectional Long Short Term Memory, Bi-LSTM) 10
2.4.3 注意力機制(Attention Mechanism) 13
2.5 貝氏神經網路(Bayesian Neural Network, BNN) 13
第三章研究方法 17
3.1 數據與資料預處理 18
3.1.1 併購成功指標 18
3.1.2 數值資料 19
3.1.3 文本資料 22
3.2 詞嵌入(Word Embedding) 22
3.3 分層注意力網路(Hierarchical Attention Network, HAN) 23
3.3.1 單詞級別 23
3.3.2 句子級別 24
3.4 MD&A時間變化量 26
3.5 貝氏神經網路(Bayesian Neural Network, BNN) 26
3.6 評估指標 28
第四章實驗結果 29
4.1 實驗設計 29
4.1.1 資料集分割 29
4.1.2 輸入變量組合與訓練參數設計 29
4.1.3 最佳化模型配置 31
4.2 實驗分析與評估 32
4.2.1 探討不同輸入組合在使用貝氏神經網路下之預測績效 32
4.2.2 探討各輸入組合在不同神經網路下之預測績效 35
4.2.3 貝氏神經網路之不確定性 37
4.3 文本注意力解釋 37
第五章結論與未來研究 40
參考文獻 42
附錄一財務指標計算公式 45

參考文獻

[1] Andrieu, C., De Freitas, N., Doucet, A., & Jordan, M. I. (2003). An introduction to MCMC for machine learning. Machine learning, 50, 5-43.
[2] Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
[3] Asudani, D. S., Nagwani, N. K., & Singh, P. (2023). Impact of word embedding models on text analytics in deep learning environment: a review. Artificial Intelligence Review, 1-81.
[4] Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93.
[5] Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American statistical Association, 112(518), 859-877.
[6] Branch, B., Wang, J., & Yang, T. (2008). A note on takeover success prediction. International Review of Financial Analysis, 17(5), 1186-1193.
[7] Calipha, R., Tarba, S., & Brock, D. (2010). Mergers and acquisitions: A review of phases, motives, and success factors. Advances in mergers and acquisitions, 9, 1-24.
[8] Cartwright, S., & Schoenberg, R. (2006). Thirty years of mergers and acquisitions research: Recent advances and future opportunities. British journal of management, 17(S1), S1-S5.
[9] Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Making words work: Using financial text as a predictor of financial events. Decision Support Systems, 50(1), 164-175.
[10] Cohen, L., Malloy, C., & Nguyen, Q. (2020). Lazy prices. The Journal of finance, 75(3), 1371-1415.
[11] Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421.
[12] Der Kiureghian, A., & Ditlevsen, O. (2009). Aleatory or epistemic? Does it matter? Structural safety, 31(2), 105-112.
[13] Fang, R., Fang, D., Guo, P., Li, Y., & Lu, Z. (2011). Motivation to Mergers and Acquisitions of High Technology Firms: Grounded in Integration Theory of Resources and Capabilities. 2011 International Conference of Information Technology, Computer Engineering and Management Sciences, 286-289.
[14] Ghosh, S., & Naskar, S. K. (2022). Detecting context-based in-claim numerals in Financial Earnings Conference Calls. International Journal of Information Technology, 14(5), 2559-2566.
[15] Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks, 18(5-6), 602-610.
[16] Guo, L., Shi, F., & Tu, J. (2016). Textual analysis and machine leaning: Crack unstructured data in finance and accounting. The Journal of Finance and Data Science, 2(3), 153-170.
[17] 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.
[18] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[19] Homburg, C., & Bucerius, M. (2006). Is speed of integration really a success factor of mergers and acquisitions? An analysis of the role of internal and external relatedness. Strategic management journal, 27(4), 347-367.
[20] Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, 40(2), 806-841.
[21] Kang, T., Park, D.-H., & Han, I. (2018). Beyond the numbers: The effect of 10-K tone on firms’ performance predictions using text analytics. Telematics and Informatics, 35(2), 370-381.
[22] Leepsa, N., & Mishra, C. S. (2017). Predicting the success of mergers and acquisitions in manufacturing sector in India: A logistic analysis. Singapore Management Journal, 6(2), 44-73.
[23] Levine, P., & Aaronovitch, S. (1981). The financial characteristics of firms and theories of merger activity. The Journal of Industrial Economics, 149-172.
[24] Liu, R., Mai, F., Shan, Z., & Wu, Y. (2020). Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach. Information & Management, 57(8), 103387.
[25] Liu, Z., Huang, D., Huang, K., Li, Z., & Zhao, J. (2021). Finbert: A pre-trained financial language representation model for financial text mining. Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, 4513-4519.
[26] Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of finance, 66(1), 35-65.
[27] MacKay, D. J. (1992). A practical Bayesian framework for backpropagation networks. Neural computation, 4(3), 448-472.
[28] Mitros, J., & Mac Namee, B. (2019). On the validity of Bayesian neural networks for uncertainty estimation. arXiv preprint arXiv:1912.01530.
[29] Nwankpa, C., Ijomah, W., Gachagan, A., & Marshall, S. (2018). Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378.
[30] Palepu, K. G. (1986). Predicting takeover targets: A methodological and empirical analysis. Journal of accounting and economics, 8(1), 3-35.
[31] Porter, M. E. (1985). Technology and competitive advantage. Journal of business strategy, 5(3), 60-78.
[32] Renneboog, L., & Vansteenkiste, C. (2019). Failure and success in mergers and acquisitions. Journal of Corporate Finance, 58, 650-699.
[33] Risberg, A. (2003). The merger and acquisition process. Journal of international business studies, 34(1), 1-34.
[34] Roztocki, N., & Needy, K. (1999). EVA for small manufacturing companies. Proceedings from the 1999 SAM International Management Conference, 461-469.
[35] Selva Birunda, S., & Kanniga Devi, R. (2021). A review on word embedding techniques for text classification. Innovative Data Communication Technologies and Application: Proceedings of ICIDCA 2020, 267-281.
[36] Sirower, M. L., & O′Byrne, S. F. (1998). The measurement of post‐acquisition performance: toward a value‐based benchmarking methodology. Journal of applied corporate finance, 11(2), 107-121.
[37] Wang, K., Du, H., Jia, R., & Jia, H. (2022). Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction. Sustainability, 14(19), 12683.
[38] Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, 1480-1489.
[39] Yao, L. J., Sutton, S. G., & Chan, S. H. (2009). Wealth creation from information technology investments using the EVA®. Journal of Computer Information Systems, 50(2), 42-48.
[40] 張晉嘉. (2008). 以類神經網路預測企業併購成敗

指導教授

葉英傑(Ying-chieh Yeh)

審核日期

2023-7-18

推文