臺灣博碩士論文加值系統

財務危機在文獻上的定義是指企業之現金流量無法償還債務，而這樣的情形是發生在企業破產或是清算之前的。財務危機的可能性對於公司本身與金融放款或是投資部門來說都是一項重要的指標，本研究基於混合模型的架構，來建立企業財務危機預測模型，提前偵測企業未來之財務風險，提供企業超前部署的機會。
本研究的分析對象為台灣電子業的上市上櫃公司，研究期間為2000年到2018年，使用了年報上的財務資訊以及總體經濟的資料來作為模型的特徵，並透過資料前處理以及特徵工程的方式，來建立資料集。在模型評比的標準上，由於財務危機發生的機會較小，因此本研究選用了召回率（Recall）、F1值以及AUC來因應類別不平衡的狀況。實證結果說明，不管是以隨機森林作為最終分類器或是以類神經網路為分類器的混合模型，其模型表現皆優於單一模型；而在變數的解釋性上，我們發現獲利能力以及經營表現的相關資訊，對於企業財務危機的預測最具有影響力。

Financial distress is defined in the literature as a situation in which the cash flow of a business is unable to repay its debts, and this occurs before the business goes bankrupt or is liquidated. The possibility of financial distress is an important indicator for the company itself as well as for the financial lending or investment departments. This study is based on the framework of hybrid model to build a predictive model of financial distress, detect the future financial risk of the company in advance and provide the opportunity for the company to deploy ahead of schedule.
In this study, the target of analysis is listed companies in Taiwan's electronics industry, the study period is from 2000 to 2018, financial information from annual reports and data from the macroeconomy are used to characterize the model, and the data set is built through data pre-processing and feature engineering. On the model performance, the recall rate, F1 score, and AUC were chosen to account for the categorical imbalance because of the low probability of financial distress. The empirical results show that the model performance is better than that of a single model, whether it is a hybrid model with a random forest as the final classifier or a neural network-like classifier. In terms of the explanatory of variables, we find that information about profitability and operating performance is most influential in predicting the financial crisis of the firm.

中文摘要 I
Abstract II
誌謝 III
⽬錄 IV
表⽬錄 VIII
圖⽬錄 IX
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究架構 3
第二章 文獻回顧 4
2.1 財務危機之定義 4
2.2 變數選取與資料結構 4
2.3 傳統預測模型 5
2.4 監督式學習 6
2.4.1 機器學習分類器 6
2.4.2 深度學習分類器 8
2.5 非監督式學習 8
2.5.1 主成份分析 8
2.6 混合模型 9
第三章 研究方法 10
3.1 研究流程 10
3.2 模型建立 11
3.2.1 超參數選取 11
3.2.2 羅吉斯迴歸 12
3.2.3 支持向量機 13
3.2.4 隨機森林 18
3.2.5 類神經網路 21
3.2.6 主成分分析 24
3.3 混合模型架構 26
3.4 模型表現準則 29
第四章 實證結果 32
4.1 研究工具與資料概況 32
4.1.1 研究工具 32
4.1.2 資料結構 33
4.1.3 資料來源與區間 33
4.2 資料特徵 34
4.2.1 變數選擇 34
4.2.2 資料描述 35
4.2.3 訓練集與測試集 36
4.2.4 敘述統計 37
4.3 資料處理與特徵工程 39
4.3.1 資料前處理 39
4.3.2 相關性與共線性分析 42
4.4 單一模型結果 45
4.4.1 非監督式學習結果 45
4.4.2 超參數數選取 45
4.4.3 變數顯著性與重要性 47
4.4.4 監督式學習預測結果 50
4.5 混合模型預測結果 52
4.5.1 超參數選取 52
4.5.2 混淆矩陣分析 53
4.5.3 模型表現分析 54
4.5.4 變數重要性 56
第五章 結論與建議 57
5.1 研究結論 57
5.2 未來建議 58
參考文獻 59
附錄一 變數分佈盒狀圖 63
附錄二 變數分佈直條圖 64
附錄三 變數相關係數圖 65

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/bf00058655
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261–11272. https://doi.org/10.1016/j.eswa.2011.02.173
Chen, W., Ma, C., & Ma, L. (2009). Mining the customer credit using hybrid support vector machine technique. Expert Systems with Applications, 36(4), 7611–7616. https://doi.org/10.1016/j.eswa.2008.09.054
Choi, H., Son, H., & Kim, C. (2018). Predicting financial distress of contractors in the construction industry using ensemble learning. Expert Systems with Applications, 110, 1–10. https://doi.org/10.1016/j.eswa.2018.05.026
Damodaran, A. (2002). Investment Valuation 2nd Edition University with Investment Set.
Durand, D., Durand, & David. (1941). Risk Elements in Consumer Instalment Financing.
Ghodselahi, A. (2011). A Hybrid Support Vector Machine Ensemble Model for Credit Scoring. In International Journal of Computer Applications (Vol. 17, Issue 5). http://archive.ics.uci.edu/ml/datasets/Statlog+
Hernandez Tinoco, M., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394–419. https://doi.org/10.1016/j.irfa.2013.02.013
Ho, T. K. (1995). Random decision forests. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1, 278–282. https://doi.org/10.1109/ICDAR.1995.598994
Hsieh, N. C. (2005). Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications, 28(4), 655–665. https://doi.org/10.1016/j.eswa.2004.12.022
Hua, Z., Wang, Y., Xu, X., Zhang, B., & Liang, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434–440. https://doi.org/10.1016/j.eswa.2006.05.006
Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847–856. https://doi.org/10.1016/j.eswa.2006.07.007
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2000). An introduction to Statistical Learning. In Current medicinal chemistry (Vol. 7, Issue 10). https://doi.org/10.1007/978-1-4614-7138-7
Kingma, D. P., & Ba, J. L. (2015, December 22). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings.
Kisman, Z., & Krisandi, D. (2019). How to Predict Financial Distress in the Wholesale Sector: Lesson from Indonesian Stock Exchange. Journal of Economics and Business, 2(3), 569–585. https://doi.org/10.31014/aior.1992.02.03.109
Laitinen, E. K. (1999). Predicting a corporate credit analyst’s risk estimate by logistic and linear models. International Review of Financial Analysis, 8(2), 97–121. https://doi.org/10.1016/S1057-5219(99)00012-5
Lee, T. S., & Chen, I. F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 28(4), 743–752. https://doi.org/10.1016/j.eswa.2004.12.031
Li, K., Niskanen, J., Kolehmainen, M., & Niskanen, M. (2016). Financial innovation: Credit default hybrid model for SME lending. Expert Systems with Applications, 61, 343–355. https://doi.org/10.1016/j.eswa.2016.05.029
Lin, S. L. (2009). A new two-stage hybrid approach of credit risk in banking industry. Expert Systems with Applications, 36(4), 8333–8341. https://doi.org/10.1016/j.eswa.2008.10.015
Lin, T. H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72(16–18), 3507–3516. https://doi.org/10.1016/j.neucom.2009.02.018
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259
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. https://doi.org/10.1016/j.eswa.2004.12.008
Myers, J. H., & Forgy, E. W. (1963). The development of numerical credit evaluation systems. Journal of the American Statistical Association, 58(303), 799–806. https://doi.org/10.1080/01621459.1963.10500889
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395
Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance, 26(2), 184–199. https://doi.org/10.1007/bf02755985
Trujillo-Ponce, A., Samaniego-Medina, R., & Cardone-Riportella, C. (2014). Examining what best explains corporate credit risk: accounting-based versus market-based models. Journal of Business Economics and Management, 15(2), 253–276. https://doi.org/10.3846/16111699.2012.720598
Vapnik, V. (1995). The nature of statistical learning theory. In The Nature of Statistical Learning Theory. Springer New York. https://doi.org/10.1007/978-1-4757-2440-0
Vapnik, V. (1998). The Support Vector Method of Function Estimation. In Nonlinear Modeling. Springer US. https://doi.org/10.1007/978-1-4615-5703-6_3
Whitaker, R. B. (1999). The early stages of financial distress. Journal of Economics and Finance, 23(2), 123–132. https://doi.org/10.1007/bf02745946
Wruck, K. H. (1990). Financial distress, reorganization, and organizational efficiency. Journal of Financial Economics, 27(2), 419–444. https://doi.org/10.1016/0304-405X(90)90063-6
Yeh, C. C., Chi, D. J., & Lin, Y. R. (2014). Going-concern prediction using hybrid random forests and rough set approach. Information Sciences, 254, 98–110. https://doi.org/10.1016/j.ins.2013.07.011