公司治理指標在財務危機預測: 以美國上市公司為例

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姓名

田馥慈(FU-TZU TIEN) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

公司治理指標在財務危機預測: 以美國上市公司為例
(Corporate government indicators apply in financial distressed problem: taking US-listed company for example)

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摘要(中)

財務危機預測問題一直以來都是重要且被廣泛討論的問題，包含分類器的使用及特徵篩選方法的選擇，都是全球金融界及研究學者關注的問題。我們過去已在台灣公司資料下證明出使用公司治理指標及財務指標當特徵會比單純用財務指標當特徵得到較好的預測結果。但卻發現美國公司方面的探討並不完整，因此我們想知道在美國公司資料下，將公司治理指標加入以財務指標為主的特徵集中，是否也可以改善預測準確率？
　　在本研究中，利用z_score及block作為特徵，提出一個利用門檻值判斷危機公司的Rule-based預測模型。在美國公司資料下，能顯著提升原本只使用z_score的模型的準確率。同時，本論文也介紹如何手動收集美國公司的CGIs的方法。

摘要(英)

Financial distress problem (FDP) has been important and widely studied topic. According different classifiers, feature selection methods even the ensemble learning have been discussed.
The past research had proved CGIs can improve predict model in Taiwan firms dataset, so we want to know if CGIs could improve predict model in US firms dataset.
We use z-score and block to be feature, and propose a new model to judge company by threshold. It can improve the accuracy of FDP in US dataset. We also introduce how to hand collect CGIs of US company.

關鍵字(中)

★ 財務危機預測
★ 公司治理指標

關鍵字(英)

★ Financial distress problem
★ corporate governance indicators

論文目次

目錄
摘要 i
Abstract ii
圖目錄 vi
表目錄 vii
一、緒論 1
1.1. 研究背景 1
1.2. 研究動機 2
1.3. 研究目的 3
1.4. 研究貢獻 3
1.5. 論文架構 4
二、文獻探討 5
2.1. FDP相關研究文獻探討 5
2.2. CGIs相關研究文獻探討 5
2.3. 傳統建模方法 9
2.3.1. 特徵篩選方法 9
2.3.1.1. t-Test 10
2.3.1.2. Stepwise Discriminant Analysis (SDA) 10
2.3.1.3. Stepwise Logistic Regression (SLR) 11
2.3.2 分類器 11
2.3.2.1. 支持向量機 13
2.3.2.2. CART 14
2.3.3. 模型驗證方法 15
2.3.4. Misclassification cost、performance metrics和cost ratios 17
三、研究資料集 19
3.1. 資料來源 19
3.2. 實驗選用的CGI 20
3.3. CGIs收集流程 23
3.4. 資料前處理 26
四、CGI加入以FR為主的特徵集在傳統建模下的可行性實驗 27
4.1. 系統架構 27
4.2. CGI加入以FR為主的特徵集在傳統建模方法實驗 28
4.2.1. 實驗參數 28
4.2.2. 實驗1: 傳統建模方法 29
4.2.3. 實驗1結果分析 30
4.3. CGI對FR當特徵判斷公司的模型改善進一步分析: 以block和z_score為例 36
4.3.1. 特徵討論 36
4.3.2. 實驗2-1: 獨立建模-1 37
4.3.3. 實驗2-2: 獨立建模-2 37
4.3.4. 實驗2結果分析 38
五、推薦之新方法: Rule-based判斷方法 41
5.1. 演算法及系統架構 41
5.1.1. 尋找門檻值演算法 42
5.1.2. 判斷公司演算法 47
5.1.3. Rule-based判斷方法系統架構圖 48
5.2. 實驗與討論 50
5.2.1. 觀察訓練資料分布 50
5.2.2. 設定條件 54
5.2.3. 實驗3: Rule-based判斷方法 55
5.2.4. 實驗3結果分析 55
六、結論與未來展望 58
6.1 結論 58
6.2 未來展望 59
參考文獻 60
附錄一-財務指標表 64
附錄二-公司配對表 66

參考文獻

參考文獻
[1] Jensen, M. C., and W. H. Meckling. 1976. Theory of the Firm：Managerial Behavior, Agency Costs and Ownership Structure, Journal of Financial Economics:305-360
[2] 蔡信夫, 鍾惠民, and 林詩韻. "控制股東代理問題與盈餘資訊內涵之關聯性研究—以台灣上市公司為例." Journal of Contemporary Accounting 4.2 (2003): 143-168.
[3] Shleifer, Andrei, and Robert W. Vishny. "A survey of corporate governance."The journal of finance 52.2 (1997): 737-783.
[4] Sun, Ji, et al. "Ownership, capital structure and financing decision: Evidence from the UK." The British Accounting Review (2015).
[5] Chen, Ming-Yuan. "Determinants of corporate board structure in Taiwan."International Review of Economics & Finance 32 (2014): 62-78.
[6] J. Collier and R. Esteban, "Governance in the participative organisation: freedom, creativity and ethics," Journal of Business Ethics, vol. 21, pp. 173-188, 1999.
[7] Y.-H. Yeh and T. Woidtke, "Commitment or entrenchment?: Controlling shareholders and board composition," Journal of Banking & Finance, vol. 29, pp. 1857-1885, 2005.
[8] Q. Liang, P. Xu, and P. Jiraporn, "Board characteristics and Chinese bank performance," Journal of Banking & Finance, vol. 37, pp. 2953-2968, 2013.
[9] H. Berkman, R. A. Cole, and L. J. Fu, "Expropriation through loan guarantees to related parties: Evidence from China," Journal of Banking & Finance, vol. 33, pp. 141-156, 2009.
[10] Y.-L. Cheung, C.-W. Chung, W. Tan, and W. Wang, "Connected board of directors: A blessing or a curse?," Journal of Banking & Finance, vol. 37, pp. 3227-3242, 2013.
[11] M. Jian and T. J. Wong, "Propping through related party transactions," Review of Accounting Studies, vol. 15, pp. 70-105, 2010.
[12] R. La Porta, F. Lopez‐de‐Silanes, and A. Shleifer, "Corporate ownership around the world," The journal of finance, vol. 54, pp. 471-517, 1999.
[13] Liang, D., Lu, C.-C., Tsai, C.-F., and Shih, G.-A. (2016) Financial Ratios and Corporate Governance Indicators in Bankruptcy Prediction: A Comprehensive Study. European Journal of Operational Research, vol. 252, no. 2, pp. 561-572.
[14] Harlan Platta, Marjorie Platt, “Corporate board attributes and bankruptcy,” Journal of Business Research, Volume 65, Issue 8, August, Pages 1139–114, 2012.
[15] P. J. Fitzpartrick, "A comparison of ratios of successful industrial enterprises with those of failed companies," Journal of Accounting Research, pp. 598-605, 1932.
[16] W. H. Beaver, "Financial ratios as predictors of failure," Journal of accounting research, pp. 71-111, 1966.
[17] E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The journal of finance, vol. 23, pp. 589-609, 1968.
[18] J. A. Ohlson, "Financial ratios and the probabilistic prediction of bankruptcy," Journal of accounting research, vol. 18, pp. 109-131, 1980.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] D. West, "Neural network credit scoring models," Computers & Operations Research, vol. 27, pp. 1131-1152, 2000.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] Abbott, Lawrence J., Susan Parker, and Gary F. Peters. "Audit committee characteristics and restatements." Auditing: A Journal of Practice & Theory23.1 (2004): 69-87.
[29] Bredart, X. (2014) Financial distress and corporate governance: the impact of board configuration. International Business Research, vol. 7, no. 3, pp. 72-80.
[30] Chen, I.-J. (2014) Financial crisis and the dynamics of corporate governance: evidence from Taiwan’s listed firms. International Review of Economics and Finance, vol. 32, pp. 3-28.
[31] Lee, T.-S. and Yeh, Y.H. (2004) Corporate governance and financial distress: evidence from Taiwan. Corporate Governance: An International Review, vol. 12, no. 3, pp. 378-388.
[32] Lin, F.-Y., Liang, D., and Chu, W.-S. (2010) The role of non-financial features related to corporate governance in business crisis prediction. Journal of Marine Science and Tehcnology, vol. 18, no. 4, pp. 504-513.
[33] Wu, J.-L. (2007) Do corporate governance factors matter for financial distress prediction of firms? Evidence from Taiwan. Master’s Thesis, University of Nottingham.
[34] 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.
[35] R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of eugenics, vol. 7, pp. 179-188, 1936.
[36] J. Neter, W. Wasserman, and M. H. Kutner, Applied linear statistical models vol. 4: Irwin Chicago, 1996.
[37] R. A. Fisher and F. Yates, "Statistical tables for biological, agricultural and medical research," Statistical tables for biological, agricultural and medical research., 1949.
[38] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, and S. Y. Philip, "Top 10 algorithms in data mining," Knowledge and Information Systems, vol. 14, pp. 1-37, 2008.
[39] 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.
[40] F. Lin, D. Liang, and E. Chen, "Financial ratio selection for business crisis prediction," Expert Systems with Applications, vol. 38, pp. 15094-15102, 2011.
[41] Lin., C.-C.C.a.C.-J.A Library for Support Vector Machines.; Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
[42] L. B. J. F. R. Olshen and C. J. Stone, "Classification and regression trees," Wadsworth International Group, 1984.
[43] 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.
[44] 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.
[45] Veliyath, Rajaram, et al. "What Do Compensation Committees on the Boards of Public Companies Do? Comparisons of Indian and US Process Differences Juxtaposing Complementary Theoretical Lenses." Long Range Planning (2015).
[46] DeZoort, F. Todd, Dana R. Hermanson, and Richard W. Houston. "Audit committee support for auditors: The effects of materiality justification and accounting precision." Journal of Accounting and Public Policy 22.2 (2003): 175-199.
[47] 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.
[48] 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.
[49] 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.

指導教授

梁德容(Deron Liang)

審核日期

2016-10-11

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