ADAPTIVE SERIAL COMBINATION MODEL OPTIMIZED USING GENETIC ALGORITHM FOR FINANCIAL DISTRESS PREDICTION

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姓名

何曼均(Maulana Hamidy Chash Chash Al Haque) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(ADAPTIVE SERIAL COMBINATION MODEL OPTIMIZED USING GENETIC ALGORITHM FOR FINANCIAL DISTRESS PREDICTION)

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摘要(中)

現如今，投資者需要通過進行財務困境預測來決定投資哪些公司，以防止損失。現有研究考慮了處理不同類別的集合，例如使用堆疊集成方法將財務比率（FRs）分為長期（LT）和短期（ST）屬性，並且另一項研究使用堆疊方法結合了Beneish M-score等額外特徵來改進。在這些研究中，長期特徵中存在某些特定的灰色區域，難以區分困境和非困境，而這可以通過使用Beneish等額外特徵來幫助預測。利用串行組合模型可以潛在地實現現有研究尚未探索的灰色區域。在本研究中，使用了一種最先進的串行組合模型，其中每個基學習器都實現了不同的特徵集。此外，串行組合中的閾值是使用一種廣泛使用的優化算法，即遺傳算法自適應優化的。使用362家台灣公司的數據，這種新穎模型可以達到與堆疊集成分類器基準相當的結果，同時提供選定的閾值，使得解釋性得以進一步探索額外特徵。結果顯示了具有競爭力的誤分類成本和公司影響分析，推薦了合適的架構。.

摘要(英)

Nowadays, investors need to decide which companies to invest in by performing financial distress predictions to prevent loss. Existing studies have considered treating distinct sets of categories, such as splitting the financial ratios (FRs) into long-term (LT) and short-term (ST) attributes using a stacking ensemble approach, and another study incorporated an additional set of features such as Beneish M-score using stacking for improvement. From these studies, there exists some specific gray area from LT features that is difficult to distinguish between distress and nondistress, which can be helped using additional features such as Beneish to predict. Utilising serial combination is potentially able to implement the existence of the gray area which existing study has not explored. In this study, a state-of-the-art serial combination model is used where each base-learner is implemented with distinct sets of features. In addition, the thresholds in the serial combination are optimized adaptively using a widely-used optimization algorithm which is the genetic algorithm. Using 362 Taiwan companies data, the novel model can achieve results as good as the stacking ensemble classifier as baseline while providing selected thresholds which allow interpretability to explore further additional features. The results have been provided with competitive misclassification costs and companies impact analysis to recommend the suitable architecture.

關鍵字(中)

★ 財務困境預測
★ 串行組合
★ 不同特徵
★ 模型優化
★ 遺傳算法

關鍵字(英)

★ financial distress prediction
★ serial combination
★ distinct features
★ model optimization
★ Genetic Algorithms (GA)

論文目次

CONTENTS 3
LIST OF FIGURES 5
LIST OF TABLES 6
ABSTRACT 6
CHAPTER I INTRODUCTION 1
1.1 Background 1
1.2 Problems Statement 3
1.3 Scopes of Problem 4
1.4 Objective 4
1.5 Advantages 4
1.6 Structure 5
CHAPTER II LITERATURE REVIEW 6
CHAPTER III THEORETICAL BASIS 16
3.1 Features in Financial Distress Prediction 16
3.2 Serial Combination 17
3.3 Logistic Regression (LR) 17
3.5 Genetic Algorithm 18
3.6 k-Fold Cross Validation 20
3.7 Evaluation Metrics 21
3.7.1 Confusion Matrix 21
3.7.2 Accuracy 21
3.7.3 Misclassification Cost 22
CHAPTER IV PROPOSED APPROACH 23
4.1 A novel serial combination architecture 23
CHAPTER V METHODOLOGY 30
5.1 Data Preparation 30
5.2 Experiment Design 31
5.3 GA Experiments Settings 32
CHAPTER VI RESULTS AND ANALYSIS 34
6.1 Results 34
6.2 Analysis 35
CHAPTER VII CONCLUSION 39
7.1 Conclusion 39
7.2 Suggestion 39
REFERENCES 40

參考文獻

Abdullah, A. M., 2016, “Comparing the Reliability of Accounting-Based and Market-based Classification Models”, Asian Journal of Accounting and Governance. http://doi.org/10.17576/ajag-2016-07-04

Altman, E. I, Iwanicz-Drozdowska, M., Laitinen, E. K. & Suvas, A., 2014, “Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman′s Z-Score Model”, Journal of International Financial Management & Accounting. http://doi.org/10.2139/ssrn.2536340

Bharath, S. T. & Shumway, T., 2008, “Forecasting Default with The Merton Distance to Default Model”, Review of Financial Studies. https://doi.org/10.1093/rfs/hhn044

Chen, M. Y., 2014, “Using A Hybrid Evolution Approach to Forecast Financial Failures for Taiwan-Listed Companies”, Quantitative Finance. http:// doi.org/10.1080/14697688.2011.618458

Chou, C. H., 2017, “Hybrid Genetic Algorithm and Fuzzy Clustering for Bankruptcy Prediction”, Applied Soft Computing. https://doi.org/10.1016/j.asoc.2017.03.014

Goodfellow, I., Bengio, Y. & Courville, A., 2016, Deep Learning, MIT Press, US.

Gorzalczany, M. B. & Rudzinski, F., 2016, “A Multi-Objective Genetic Optimization for Fast, Fuzzy Rule-Based Credit Classification with Balanced Accuracy and Interpretability”, Applied Soft Computing. http://doi.org/10.1016/j.asoc.2015.11.037

Hastie, T., Tibshirani, R. & Friedman, J., 2008, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, California, US.

Komar, M. F., Liang, D., & Rahmi, A., 2022, “Financial Distress Prediction Base on Altman Ratio and Beneish M-Score using Stacking Ensemble Learning”, National Central University, Taiwan.

Kuhn, M. & Johnson, K., 2016, Applied Predictive Modeling, Springer, New York, US.

Lahmiri, S., 2021, “An Adaptive Sequential-Filtering Learning System for Credit Risk Modeling”, Soft Computing. https://doi.org/10.1007/s00500-021-05833-y

Liang, D., 2020, “Combining Corporate Governance Indicators with Stacking Ensembles for Financial Distress Prediction”, Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.07.052

Lin, W. C., 2018, “Feature Selection in Single and Ensemble Learning‐Based Bankruptcy Prediction Models”, Expert Systems. https://doi.org/exsy.12335

Queen, M. & Roll, R., 1987, “Firm Mortality: Using Market Indicators to Predict Survival”, Financial Analysts Journal. https://doi.org/10.2469/faj.v43.n3.9

Rahmi, A., Lu, H. Y., Liang, D., Novitasari, D. & Tsai, C. F., 2022, “Role of Comprehensive Income in Predicting Bankruptcy”, Computational Economics. https://doi.org/10.1007/s10614-022-10328-5

Rahmi, A., Liang, D., Fadilah, A. N., 2024, “Splitting Long-Term and Short-Term Financial Ratios for Improved Financial Distress Prediction: Evidence from Taiwanese Public Companies”, Journal of Forecasting. https://doi.org/10.1002/for.3143

Sreedharan, M., Khedr, A. M. & Bannany, M. E., 2020, “A Comparative Analysis of Machine Learning Classifiers and Ensemble Techniques in Financial Distress Prediction”, 17th International Multi-Conference on Systems, Signals & Devices. https://doi.org/10.1109/SSD49366.2020.9364178

Sun, J. & Li, H., 2009, “Financial Distress Prediction Based on Serial Combination of Multiple Classifiers”, Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2008.10.002

Tan, P. N., Steinbach, M. & Kumar, V., 2014, Introduction to Data Mining, Pearson, Harlow, England.

Taylor, B. W., 2013, Introduction to Management Science, Pearson, New Jersey, US.

Tsai, C. F. & Sung, Y. T., 2020, “Ensemble Feature Selection in High Dimension, Low Sample Size Datasets: Parallel and Serial Combination Approaches”, Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2020.106097

指導教授

梁德容(Deron Liang)

審核日期

2024-8-21

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