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：9

、訪客IP：3.135.204.121

姓名	格普利(Gde Putu Rizkynindra Sukma Jati)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	基於 Altman 和基於市場的綜合變量使用Stacking Ensemble Learning預測財務困境 (Stacking Ensemble Learning for Financial Distress Prediction Based on Altman & Comprehensive Market-Based Variables)
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]   至系統瀏覽論文 (2025-7-21以後開放)
摘要(中)	以往的研究廣泛使用財務比率和市場變數來構建財務困境預測模型。 Altman ‘s Z-Score在學術研究中已成為預測破產最常用的財務比率。 可以根據公司在市場上的一些變數（公司規模、市值、總回報、回報波動性、貝塔係數、違約距離和違約概率）預測公司現有的財務困境。 也有一些研究常見著名的機器學習算灋預測破產，但是之前沒有任何研究試圖將這七個基於市場的變數作為綜合市場變數，與集成學習方法相結合去預測破產的Altman分數。 以前的研究認為，stacking ensemble learning方法比單個分類器更能提高預測結果。 本研究旨在使用stacking ensemble learning方法結合邏輯回歸於5個Altman變數和7個基於市場的綜合變數作為預測因數，預測臺灣公司的財務困境。 現時的結果表明，與僅使用5個Altman變數相比，將7個基於市場的綜合變數添加到5個Altman變數中可以提高財務困境預測的效能。 此外，本研究還發現7個綜合市場變數和5個Altman變數之間存在著很强的聯系。 本研究的結果將解釋哪些基於市場的綜合變數會影響預測結果。 這些發現還將通過提供有關財務比率和市場變數的資訊，幫助公共投資做出貸款決策。
摘要(英)	Previous research had extensively used financial ratios and market variables to build their model of financial distress Prediction. Altman′s Z-Score has become the most commonly used financial ratio for identify bankruptcy, particularly in academic studies. Firm size, market cap, total return, return volatility, beta, distance to default, and probability to default are market-based variables that can forecast financial distress. The previous study also predicted bankruptcy using several well-known machine learning algorithms. However, no previous research attempted to combine these seven market-based variables as comprehensive market variables with Altman variables using the stacking ensemble learning method to predict bankruptcy. Prior studies believe that the stacking ensemble learning method can improve the Prediction result more than a single classifier. This study aims to use the stacking ensemble learning method in conjunction with Logistic Regression to predict financial distress in Taiwanese companies by combining 5 Altman variables and 7 comprehensive market-based variables as predictors. The result shows that adding the 7 comprehensive market-based variables to 5 Altman variables improves the performance of financial distress Prediction than only using 5 Altman variables. Also, this research found a strong connection between 7 comprehensive market-based variables and 5 Altman variables. The findings of this study will explain which comprehensive market-based variables influence the Prediction outcome. These findings will also aid public investment in lending decisions by providing information on financial ratios and market variables.
關鍵字(中)	★ Altman Variables ★ 財務困境預測 ★ Stacking Ensemble Learning ★ market-based variables	關鍵字(英)	★ Altman Variables ★ Financial Distress Prediction ★ Stacking Ensemble Learning ★ market-based variables
論文目次	Table of Contents 摘要 i ABSTRACT ii ACKNOWLEDGMENT iii Table of Contents iv List of Figures vi List of Tables vii 1 Introduction 1 1.1 Background 1 1.2 Problem Statements 2 1.3 Outline Chapters 2 1.4 Research Limitations 2 2 Literature Review 3 2.1 Altman Variables 3 2.2 Market-Based Variables 4 2.3 Logistic Regression 4 2.4 Support Vector Machines 5 2.5 Linear Discriminant Analysis 7 2.6 K-Nearest Neighbor 8 2.7 Bagging Tree 8 2.8 Principal Component Analysis (PCA) 9 2.9 Ensemble of Classifiers 10 2.9.1 Generating Base Classifier 10 2.9.2 Integrating Decisions 11 2.9.3 Stacking Generalization 12 3 Research Method 14 3.1 Experiment Architecture 14 3.2 Dataset 15 3.3 Data Pre-processing 18 3.3.1 Defining Crisis Company 18 3.3.2 Matching Method 19 3.4 Experiment Design 21 3.5 Model Building 23 3.6 Evaluation Metrics 24 3.7 Result Overview 25 3.7.1 DET Curve 25 3.7.2 Wilcoxon Test 26 3.8 Experiment Settings 27 4 Experiment Result 29 4.1 Experiment Result for Baseline 32 4.2 Experiment Result for Proposed Model Monthly (M1) 33 4.3 Experiment Result for Proposed Model Yearly (M2) 35 4.4 Discussion 36 5 Conclusion and Suggestion 41 5.1 Conclusion 41 5.2 Suggestion 41 6 Bibliographies 42 7 Appendix A 44
參考文獻	[1] M. Queen and R. Roll, “Firm Mortality: Using Market Indicators to Predict Survival.” [2] F. van der Colff and L. Brummer, “Financial distress prediction using a machine learning model- A study of JSE-listed companies”. [3] L. H. Chen and H. der Hsiao, “Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study,” Expert Systems with Applications, vol. 35, no. 3, pp. 1145–1155, Oct. 2008, doi: 10.1016/j.eswa.2007.08.010. [4] Z. Hua, Y. Wang, X. Xu, B. Zhang, and L. Liang, “Predicting corporate financial distress based on integration of support vector machine and logistic regression,” Expert Systems with Applications, vol. 33, no. 2, pp. 434–440, Aug. 2007, doi: 10.1016/j.eswa.2006.05.006. [5] N. Santoso and W. Wibowo, “Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine,” in Journal of Physics: Conference Series, Mar. 2018, vol. 979, no. 1. doi: 10.1088/1742-6596/979/1/012089. [6] O. ULUDAĞ and A. GÜRSOY, “Financial Risk Estimation with KNN Classification Algorithm on Determined Financial Ratios,” European Journal of Science and Technology, Dec. 2021, doi: 10.31590/ejosat.1001663. [7] F. H. TUNIO, Y. DING, A. N. AGHA, K. AGHA, and H. U. R. Z. PANHWAR, “Financial Distress Classification Using Adaboost and Bagging in Pakistan Stock Exchange,” Journal of Asian Finance, Economics and Business, vol. 8, no. 1, pp. 665–673, 2021, doi: 10.13106/jafeb.2021.vol8.no1.665. [8] F. Lin, D. Liang, and W. S. Chu, “The role of non-financial features related to corporate governance in business crisis classification,” Journal of Marine Science and Technology, 2010. [9] E. I. Altman, M. Iwanicz-Drozdowska, E. K. Laitinen, and A. Suvas, “Distressed Firm and Bankruptcy Classification in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model,” SSRN Electronic Journal, 2014, doi: 10.2139/ssrn.2536340. [10] A. M. Abdullah, “Comparing the Reliability of Accounting-Based and Market-based Classification Models,” Asian Journal of Accounting and Governance, vol. 7, pp. 41–55, Nov. 2016, doi: 10.17576/ajag-2016-07-04. [11] S. T. Bharath and T. Shumway, “Forecasting default with the Merton distance to default model,” Review of Financial Studies, vol. 21, no. 3, pp. 1339–1369, May 2008, doi: 10.1093/rfs/hhn044. [12] J. A. Ohlson, “Financial Ratios and the Probabilistic Classification of Bankruptcy,” 1980. [13] D. W. Hosmer, Stanley. Lemeshow, and R. X. Sturdivant, Applied logistic regression. [14] C. Cortes, V. Vapnik, and L. Saitta, “Support-Vector Networks Editor,” Kluwer Academic Publishers, 1995. [15] A. K. Qin, S. Y. M. Shi, P. N. Suganthan, and M. Loog, “Enhanced Direct Linear Discriminant Analysis for Feature Extraction on High Dimensional Data,” 2005. [Online]. Available: www.aaai.org [16] T. M. Cover and P. E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967, doi: 10.1109/TIT.1967.1053964. [17] L. Bbeiman, “Bagging Predictors,” 1996. [18] M. Y. Chen, “Predicting corporate financial distress based on integration of decision tree classification and logistic regression,” Expert Systems with Applications, vol. 38, no. 9, pp. 11261–11272, Sep. 2011, doi: 10.1016/j.eswa.2011.02.173. [19] M. P. Sesmero, A. I. Ledezma, and A. Sanchis, “Generating ensembles of heterogeneous classifiers using Stacked Generalization,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 5, no. 1, pp. 21–34, Jan. 2015, doi: 10.1002/widm.1143. [20] W. Jiang, Z. Chen, Y. Xiang, D. Shao, L. Ma, and J. Zhang, “SSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification,” IEEE Access, vol. 7, pp. 120337–120349, 2019, doi: 10.1109/access.2019.2933262. [21] W. Jiang, Z. Chen, Y. Xiang, D. Shao, L. Ma, and J. Zhang, “Ssem: A novel self-adaptive stacking ensemble model for classification,” IEEE Access, vol. 7, pp. 120337–120349, 2019, doi: 10.1109/ACCESS.2019.2933262. [22] S. Džeroski and B. Ženko, “Is combining classifiers with stacking better than selecting the best one?,” Machine Learning, vol. 54, no. 3, pp. 255–273, Mar. 2004, doi: 10.1023/B:MACH.0000015881.36452.6e. [23] E. Menahem, L. Rokach, and Y. Elovici, “Troika-An Improved Stacking Schema for Classification Tasks.”
指導教授	1. 王尉任 2. 梁德容 3. 盧佳琪 Retantyo Wardoyo(Wei-Jen Wang De-Ron Liang Chia-Chi Lu Retantyo Wardoyo)	審核日期	2022-7-27
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare