漸進式模型應用於財務危機預測問題

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：8

、訪客IP：18.217.144.32

姓名

陳以理(Yi-li Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

漸進式模型應用於財務危機預測問題
(Financial Crisis Prediction Problem Based on Time Series Model)

相關論文

★ 基於最大期望算法之分析陶瓷基板機器暗裂破片率	★ 基於時間序列預測的機器良率預測
★ 基於OpenPose特徵的行人分心偵測	★ 建構深度學習CNN模型以正確分類傳統AOI模型之偵測結果
★ 一種結合循序向後選擇法與回歸樹分析的瑕疵肇因關鍵因子擷取方法與系統－以紡織製程為例	★ 融合生成對抗網路及領域知識的分層式影像擴增
★ 針織布異常偵測方法研究	★ 基於工廠生產資料的異常機器維修預測
★ 萃取駕駛人在不同環境之駕駛行為方法	★ 基於刮痕瑕疵資料擴增的分割拼接影像生成
★ 應用卷積神經網路於航攝影像做基於坵塊的水稻判釋之研究	★ 採迴歸樹進行規則探勘以有效同時降低多種紡織瑕疵
★ 應用增量式學習於多種農作物判釋之研究	★ 應用自動化測試於異質環境機器學習管道之 MLOps 系統
★ 農業影像二元分類：坵塊分離的檢測	★ 應用遷移學習於胚布瑕疵檢測

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)

財務危機預測問題(Financial crisis prediction preblem)已被廣泛地討論，不論是分類器的選擇、特徵選取的方法，抑或是各種分類器的組合使用，都是全球金融圈及學術界討論的方向。
以往大部分的學者研究FCP時均使用靜態的N-Fold模型分析FCP問題，沒有考慮到FCP問題是有Time series的特性，在Data mining領域已對具有Time series特性的問題作討論，且近年來開始有學者使用Time series模型分析FCP問題。
此外以往在台灣地區的FCP研究多直接使用台灣經濟新報(TEJ)對危機公司的分類，但我們認為僅「有實際造成損失」之危機公司才是需要被預測出來的，其餘屬於「無實際造成損失」之危機公司可以用來作為Training set的一員，幫助找出「有實際造成損失」之危機公司，本身並不需加入Type I error的計算。
我們用實驗證明我們所提出的Modeling strategy對於N-Fold模型，以及漸進式模型都可以提升模型的準確率，並且發現在台灣資料集下使用我們所推薦的Modeling strategy並用漸進式模型建模時，傳統認為機器學習準確率顯著優於統計方法的觀點無法成立，我們也試圖找出該觀點無法成立的原因，發現不同Modeling strategy所占原因不高，漸進式模型才是主要原因。

摘要(英)

Financial crisis prediction problem (FCP problem) has been important and widely studied, many different classifiers, feature selection methods even the ensemble learning have been discussed.
In the past, most researcher use static model to solve FCP problem, which is N-Fold model, they did not consider that FCP problem has time series characteristics. As in the data mining domain, some scholars have been discussed the issue about time series problem must use time series model to solve. Recently, some researchers began to ues time series model on FCP problem.
On the other side, the FCP problem studied in Taiwan, the definition of crisis was using the TEJ database as a source, but we find that only the firms that cause actual losses should be consider as crisis firms, other kinds of crisis firms can only be used as training data for we to find the actual losses firm, when computing the Type I error this kinds of crisis firms don’t need to be counted, this is our proposed modeling strategy (PMS).
Finally, our experiment result shows that our PMS outperform the traditional modeling strategy, both in N-fold model and in Time series model. And for Taiwan dataset, using our PMS in Time series model the traditional wisdom, which is “machine learning approaches outperform the statistical methods”, does not hold. We are also trying to figure out why it doesn′t still hold, the experiment result show that the main reason is not the different modeling strategy, the main reason is Time series model.

關鍵字(中)

★ 統計方法
★ 機器學習方法
★ 財務危機預測
★ 漸進式模型
★ 危機公司定義

關鍵字(英)

★ statistic mothod
★ machine learning
★ financial crisis prediction
★ time series
★ multiperiod
★ definition of crisis

論文目次

中文摘要 i
Abstract ii
圖目錄 vi
表目錄 viii
一、緒論 1
1.1. 研究背景 1
1.2. 研究動機 2
1.3. 研究目的 7
1.4. 論文架構 7
二、文獻探討 8
2.1. 危機公司定義相關研究文獻探討 8
2.2. FCP問題相關研究文獻探討 9
2.3. Time series相關研究文獻探討 9
2.4. 統計方法介紹 11
2.4.1. Linear Regression(LR) 11
2.4.2. Logistic Regression(Logit) 12
2.4.3. Discriminant analysis(DA) 13
2.5. 機器學習方法介紹 14
2.5.1. 支持向量機(SVM) 14
2.5.2. 類神經網路(NN) 18
2.6. 特徵挑選方法 19
2.6.1. Stepwise Discriminant Analysis (SDA) 20
2.6.2. Stepwise Logistic Regression (SLR) 21
2.6.3. t-Test 22
三、系統架構 23
3.1. Traditional Modeling Strategy(TMS) 23
3.2. Proposed Modeling Strategy(PMS) 23
3.3. N-Fold模型 24
3.4. 漸進式模型 25
四、實驗設計 27
4.1. 資料來源 27
4.2. 資料前置處理方式 29
4.3. Misclassification cost、performance metrics和cost ratios 30
4.4. 實驗參數 31
4.5. 研究假說 33
4.6. 實驗架構 33
4.6.1. N-Fold模型使用PMS及TMS實驗設計 33
4.6.2. 漸進式模型使用PMS及TMS實驗設計 35
五、實驗流程與結果分析-1 36
5.1. 針對Modeling Strategy進行討論 36
5.2. 實驗流程 37
5.3. 實驗結果與分析 38
六、實驗流程與結果分析-2 46
6.1. 針對漸進式模型討論 46
6.2. 實驗流程 46
6.3. 實驗結果與結果分析 47
七、實驗流程與結果分析-3 52
7.1. 針對實驗結果與傳統觀點不符討論 52
7.2. 實驗流程 52
7.3. 實驗結果與分析 53
八、結論與未來展望 57
8.1. 結論 57
8.2. 未來展望 58
參考文獻 59
附錄一-財務比率表 63
附錄二-公司配對表 64
附錄三-其他實驗結果呈現 71
漸進式模型不同特徵挑選之同分類器比較 71
N-Fold模型不同特徵挑選之同分類器比較 76

參考文獻

[1] P. J. Fitzpartrick, "A comparison of ratios of successful industrial enterprises with those of failed companies," Journal of Accounting Research, pp. 598-605, 1932.
[2] W. H. Beaver, "Financial ratios as predictors of failure," Journal of accounting research, pp. 71-111, 1966.
[3] E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The journal of finance, vol. 23, pp. 589-609, 1968.
[4] J. A. Ohlson, "Financial ratios and the probabilistic prediction of bankruptcy," Journal of accounting research, vol. 18, pp. 109-131, 1980.
[5] L.-H. Chen and H.-D. 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, pp. 1145-1155, 2008.
[6] 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, pp. 434-440, 2007.
[7] K.-S. Shin, T. S. Lee, and H.-j. Kim, "An application of support vector machines in bankruptcy prediction model," Expert Systems with Applications, vol. 28, pp. 127-135, 2005.
[8] C.-H. Wu, G.-H. Tzeng, Y.-J. Goo, and W.-C. Fang, "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert systems with applications, vol. 32, pp. 397-408, 2007.
[9] D. West, "Neural network credit scoring models," Computers & Operations Research, vol. 27, pp. 1131-1152, 2000.
[10] L. Sun and P. P. Shenoy, "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, vol. 180, pp. 738-753, 2007.
[11] H. Li, J. Sun, and J. Wu, "Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods," Expert Systems with Applications, vol. 37, pp. 5895-5904, 2010.
[12] K. Y. Tam and M. Y. Kiang, "Managerial applications of neural networks: the case of bank failure predictions," Management science, vol. 38, pp. 926-947, 1992.
[13] E. N. Ozkan-Gunay and M. Ozkan, "Prediction of bank failures in emerging financial markets: an ANN approach," Journal of Risk Finance, The, vol. 8, pp. 465-480, 2007.
[14] S.-W. Lin, K.-C. Ying, S.-C. Chen, and Z.-J. Lee, "Particle swarm optimization for parameter determination and feature selection of support vector machines," Expert Systems with Applications, vol. 35, pp. 1817-1824, 2008.
[15] C.-F. Tsai, "Feature selection in bankruptcy prediction," Knowledge-Based Systems, vol. 22, pp. 120-127, 2009.
[16] W.-Y. Lin, Y.-H. Hu, and C.-F. Tsai, "Machine learning in financial crisis prediction: a survey," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 42, pp. 421-436, 2012.
[17] F. Lin, D. Liang, and E. Chen, "Financial ratio selection for business crisis prediction," Expert Systems with Applications, vol. 38, pp. 15094-15102, 2011.
[18] C.-F. Tsai and M.-L. Chen, "Credit rating by hybrid machine learning techniques," Applied Soft Computing, vol. 10, pp. 374-380, 2010.
[19] H. Liu and L. Yu, "Toward integrating feature selection algorithms for classification and clustering," Knowledge and Data Engineering, IEEE Transactions on, vol. 17, pp. 491-502, 2005.
[20] I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," The Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
[21] G. R. Iversen and H. Norpoth, Analysis of variance: Sage, 1987.
[22] E. S. Pearson, W. S. Gosset, R. L. Plackett, and G. A. Barnard, Student: a statistical biography of William Sealy Gosset: Oxford University Press, USA, 1990.
[23] W. R. Klecka, Discriminant analysis: Sage, 1980.
[24] D. Wooff, "Logistic Regression: a Self‐learning Text," Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 167, pp. 192-194, 2004.
[25] R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artificial intelligence, vol. 97, pp. 273-324, 1997.
[26] A. L. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artificial intelligence, vol. 97, pp. 245-271, 1997.
[27] S. Balcaen and H. Ooghe, "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, vol. 38, pp. 63-93, 2006.
[28] R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of eugenics, vol. 7, pp. 179-188, 1936.
[29] J. Neter, W. Wasserman, and M. H. Kutner, Applied linear statistical models vol. 4: Irwin Chicago, 1996.
[30] R. A. Fisher and F. Yates, "Statistical tables for biological, agricultural and medical research," Statistical tables for biological, agricultural and medical research., 1949.
[31] J. Efrim Boritz and D. B. Kennedy, "Effectiveness of neural network types for prediction of business failure," Expert Systems with Applications, vol. 9, pp. 503-512, 1995.
[32] N. Chen, A. S. Vieira, J. Duarte, B. Ribeiro, and J. C. Neves, "Cost-sensitive learning vector quantization for financial distress prediction," in Progress in Artificial Intelligence, ed: Springer, 2009, pp. 374-385.
[33] P. L. Brockett, L. L. Golden, J. Jang, and C. Yang, "A comparison of neural network, statistical methods, and variable choice for life insurers′ financial distress prediction," Journal of Risk and Insurance, vol. 73, pp. 397-419, 2006.
[34] O. S. Persons, "Using financial statement data to identify factors associated with fraudulent financial reporting," Journal of Applied Business Research (JABR), vol. 11, pp. 38-46, 2011.
[35] M.-J. Kim and D.-K. Kang, "Ensemble with neural networks for bankruptcy prediction," Expert Systems with Applications, vol. 37, pp. 3373-3379, 2010.
[36] T. Shumway, "Forecasting bankruptcy more accurately A simple hazard model," Journal of Business, 74, pp. 101-124, 2001.
[37] T.-H. Lin, "A cross model study of corporate financial distress prediction in Taiwan Multiple discriminant analysis, logit, probit and neural networks models," Neurocomputing, 72, pp. 3507-3516, 2009.
[38] P.-C. Ko and P.-C. Lin, "An evolution-based approach with modularized evaluations to forecast financial distress," Knowledge-Based Systems, 19, pp. 84-91, 2006.
[39] D. Duffie, L. Saita and K. Wang, "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, 83, pp. 635-665, 2007.
[40] J.-C. Duan, J. Sun and Tao. Wang, " Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, 170, pp. 191-209, 2012.
[41] I. Kaastra and M. Boyd, " Designing a neural network for forecasting financial and economic time series," Neurocomputing, 10, pp. 215-236, 1996.
[42] L.-J. Cao, and Francis E. H. Tay, " Support Vector Machine With Adaptive Parameters in financial time series forecasting," IEEE Transactions on Neural Networks, 14, no. 6, 2003.
[43] T.-C. Fu, " A review on time series data mining," Engineering Applications of Artificial Intelligence, 24, pp. 164-181, 2011.
[44] H. Li and J. Sun, "Ranking-order case-based reasoning for financial distress prediction," Knowledge-Based Systems, 21, pp. 868-878, 2008.
[45] H. Li and J. Sun, "Gaussian case-based reasoning for business failure prediction with empirical data in China," Information Sciences, 179, pp. 89-108, 2009.
[46] H. Li and J. Sun, "Business failure prediction using hybrid2 case-based reasoning (H2CBR)," Computers & Operations Research, 37, pp. 137-151, 2010.
[47] H. Li, J. Sun and J. Wu, "Predicting business failure using classification and regression tree An empirical comparison with popular classical statistical methods and top classification mining methods," Expert Systems with Applications, 37, pp. 5895-5904, 2010.
[48] Y. Ding, X. Song, and Y. Zen, "Forecasting financial condition of Chinese listed companies based on support vector machine," Expert Systems with Applications, 34, pp. 3081-3089, 2008.
[49] H. Lin, H.-B Huang, J. S and C. Lin, "On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction," Expert Systems with Applications, 37, pp. 4811-4821, 2010.
[50] H. Lin, J. S and B.-L, Sun, "Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors," Expert Systems with Applications, 36, pp. 643-659, 2009.
[51] T.-C. Tang and L.-C. Chi, "Neural networks analysis in business failure prediction of Chinese importers A between-countries approach," Expert Systems with Applications, 29, pp. 244-255, 2005.
[52] C.-H. Wu, G.-H. Tzeng, Y.-J. Goo and W.-C. Fang, "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert Systems with Applications, 32, pp. 397-408, 2007.
[53] C.-B Cheng, C.-L. Chen and C.-J. Fu, "Financial distress prediction by a radial basis function network with logit analysis learning," Computers and Mathematics with Applications, 51, pp. 579-588, 2006.
[54] W.-S.Chen and Y.-K. Du, "Using neural networks and data mining techniques for the financial distress prediction model," Expert Systems with Applications, 36, pp. 4075-4086, 2009.
[55] K.-C. Lee, I. Han and Y. Kwon, "Hybrid neural network models for bankruptcy predictions," Decision Support Systems, 18, pp. 63-72, 1996.
[56] K. Lee, D. Booth and P. Alam, "A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms," Expert Systems with Applications, 29, pp. 1-16, 2005.
[57] E. Kahya and P. Theodossiou, "Predicting Corporate Financial Distress A Time-Series CUSUM Methodology," Review of Quantitative Finance and Accounting, 13, pp. 323-345, 1999.

指導教授

梁德容(Deron Liang)

審核日期

2016-1-8

推文