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姓名	穆法德(Muhammad Fadhlil Hadi Komar)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	基於 Altman Ratio和 Beneish M-Score 的財務困境預測中使用Stacking Ensemble Learning (Financial Distress Prediction Based on Altman Ratio and Beneish M-Score using Stacking Ensemble Learning)
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)	在以往的研究中，財務比率被廣泛用於構建其財務困境預測模型。 Altman Ratio旨在衡量一家公司的財務狀況，在各種環境和市場中預測破產是準確的，在學術研究中已成為最常用的預測方法； 然而，Altman Ratio取決於財務報表中數據的有效性，需要其他變數來評估財務報告操縱的可能性。 之前的研究都沒有試圖將五個Altman Ratio與三年Beneish M-Score相結合。 我們提出了Stacking Ensemble Learning，該方法將五個Altman Ratio轉化為短期和長期變數，並在危機發生前進行3年的Beneish M-Score進行全面分析。 這些見解不僅混合了所有財務指標資訊，而且還根據長期、短期條件以及財務報表操縱的可能性仔細評估了這些資訊，從而幫助公共投資做出貸款決策。
摘要(英)	Financial Ratio had been used widely on the previous research to build their model of financial distress prediction. The Altman Ratios was become the most often used for predicting especially in academic studies. Altman Ratios purposes to measure a company’s financial health and it proven accurate to forecast bankruptcy in a wide variety of contexts and markets. However, the Altman Ratios depends on the validity of the data in the financial statements, then other variable is needed to assess the financial report manipulation possibility. None of the previous studies attempted to combine the five Altman Ratios with the 3 years Beneish M-Score. We proposed stacking ensemble learning that have an ability to threatens five Altman Ratios into Short-term and long-term variables and 3 years of Beneish M-Score before the crisis happens and performed a comprehensive analysis. These insights help public investment make lending decisions by not only mixing all financial indicator information, but also carefully assessing it based on long-term, short-term condition, and also possibility of financial statement manipulation.
關鍵字(中)	★ Altman Ratio ★ Beneish M-Score ★ 財務困境預測 ★ Stacking Ensemble Learning	關鍵字(英)	★ Altman Ratio ★ Beneish M-Score ★ Financial Distress Prediction ★ Stacking Ensemble Learning
論文目次	摘要 i ABSTRACT ii ACKNOWLEDGMENT iii Table of Contents iv List of Figures vi List of Tables vii CHAPTER I INTRODUCTION 1 1.1 Background 1 1.2 Problem Statements 2 1.3 Outline Chapters 2 1.4 Research Limitations 2 CHAPTER II LITERATURE REVIEW 4 2.1 Previous work 4 2.2 Altman Financial Ratio 5 2.3 Beneish M-Score 7 2.4 Ensembles of Classifiers 8 2.5 Stacking Ensemble Learning 9 2.6 Logistic Regression 10 2.7 K-Nearest Neighbours 11 2.8 Suppor Vector Machine 12 2.9 Bagging Tree 14 2.10 Naïve Bayes 15 2.11 Linear Discriminant Analysis 15 CHAPTER III RESARCH METHODOLOGY 17 3.1 Experiment Architecture 17 3.2 Raw Experiment Dataset 17 3.3 Data Pre-processing 19 3.3.1 Defining Crisis and Normal Company 20 3.3.2 Matching Method 21 3.4 Experiment Design 22 3.5 Model Building 24 3.6 Evaluation Metrics 25 3.7 Result Overview 26 3.7.1 DET Curve 26 3.7.2 Wilcoxon Test 27 3.8 Experiment Settings 28 CHAPTER IV EXPERIMENT RESULT AND ANALYSIS 29 4.1 Base Classifier Selection 29 4.2 Baseline Model vs Proposed Model 34 4.3 Impact analysis of M-Score on Prediction Model 36 CHAPTER V CONCLUSION AND FUTURE WORK 39 5.1 Conclusion 39 5.2 Future Work 39 Bibliographies 40 Appendix A 43
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指導教授	王尉任 梁德容 盧佳琪 Tri Kuntoro Priyambodo(Wei-Jen Wang De-Ron Liang Chia-Chi Lu Tri Kuntoro Priyambodo)	審核日期	2022-7-27
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