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題名 香港企業信用風險預警制度之建構與研究
Research of Credit Risk Prediction in Hong Kong
作者 林霈吾
Lin, Pei-Wu
貢獻者 林宛瑩
林霈吾
Lin, Pei-Wu
關鍵詞 信用風險預警
企業失敗
邏輯迴歸分析
多元判別分析
credit risk prediction
financial distress
logistic regression analysis
multiple discriminant analysis
日期 2019
上傳時間 5-Sep-2019 15:40:20 (UTC+8)
摘要 近十年的快速成長吸引了各類國際企業與投資機構進入中國市場，然而由於法制的不成熟及國家體制的特殊，舞弊層出不窮。在此背景下，香港轉而成為眾多外資的首選，然而國內外資金的流入帶來繁榮也為香港市場帶來未知數，市場對信用風險預警制度的需求日漸提高，至今卻沒有太多相關之研究。為了瞭解影響香港企業信用風險之因素，本文以2008年到2017年間的香港主板上市公司為研究對象，參考過去文獻與香港市場現況為危機事件定義，再透過邏輯迴歸、常態機率迴歸及多元判別分析進行實證研究，分別以流動性、獲利性及安全性三個面向探討有關之解釋變數，研究結果顯示多元辦別分析模型在特定組合下於危機事件發生前一年有高達66.25%的危機樣本預測準確率，而於危機事件前兩年之危機樣本預測效果則僅不到1%的下降。
Due to the high-speed of growth in China, the hot money of foreign investors pours in and the China stock market become popular in Asian. However, the weakness in laws and regulations make it risky to invest in this market. In contrast to China, the stable financial environment and relatively sound legal systems make Hong Kong a better choice for foreign companies and investors. These cash flows bring prosperity to Hong Kong, and increase the instability and the needs of credit risk prediction model in the whole market at the same time. In order to realize the factors of financial distress for Hong Kong listed company, I select some listed company during 2008-2017 as the samples. By taking the financial environment into account, I determine the definitions of distress. Then, I use logistic regression model, probit model and multiple discriminant analysis model to test the three different kinds of variables and figure out which are significant to distress prediction. The result shows that the accuracy of multiple discriminant analysis model in distress company can up to 66.25 percent.
參考文獻 牛陳才，2011，我國上市公司財務預警的實證分析——基於修正的 Z-score 模型，中國管理資訊化，23期，11-14。
周百隆與盧俊安，2007，以Cascaded Logistic Model 建構我國企業財務危機預警模型之研究，中華管理評論國際學報，第十卷，第2期。
林宓穎，2002，上市公司財務危機預警模式之研究，國立政治大學財政學系碩士論文。
林郁翎與黃建華，2009，考慮公司治理之企業財務危機預警模型。東吳經濟商學學報，(64)，23-55。
郑茂，2003，我国上市公司财务风险预警模型的构建及实证分析，金融论坛，8(10)，38-42。
黃華山與邱一薰，2005，類神經網路預測台灣50股價指數之研究，資訊、科技與社會學報，第9期，19-42。
潘春健，2006，香港創業板上市公司財務預警實證分析，嘉興學院學報，第1期，66-69。
蔡孟哲與許溪南，2018，應用類神經網路建構台灣上市上櫃公司 TCRI 信用風險模式，TANET2018 臺灣網際網路研討會，1134-1139。
盧嘉梧與林志軒，2016，類神經網路投資組合策略績效之實證研究: 以台灣中型100電子股為例，輔仁管理評論，23(3)，29-50。
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.
Altman, E. I., & Hotchkiss, E. (1993). Corporate financial distress and bankruptcy.
Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis A new model to identify bankruptcy risk of corporations. Journal of banking & finance, 1(1), 29-54.
Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of banking & finance, 18(3), 505-529.
Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111.
Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of political economy, 81(3), 637-654.
Blum, M. (1974). Failing company discriminant analysis. Journal of accounting research, 1-25.
Brown, D. T., James, C. M., & Mooradian, R. M. (1994). Asset sales by financially distressed firms. Journal of Corporate finance, 1(2), 233-257.
Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899-2939.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of accounting research, 167-179.
Doumpos, M., & Zopounidis, C. (1999). A multicriteria discrimination method for the prediction of financial distress: the case of Greece. Multinational Finance Journal, 3(2), 71-101.
Frydman, H., Altman, E. I., & Kao, D. L. (1985). Introducing recursive partitioning for financial classification: the case of financial distress. The Journal of Finance, 40(1), 269-291.
Gilson, S. C., John, K., & Lang, L. H. (1990). Troubled debt restructurings: An empirical study of private reorganization of firms in default. Journal of financial economics, 27(2), 315-353.
Hillier, D., Ross, S., Westerfield, R., Jaffe, J., & Jordan, B. (2013). Corporate finance (No. 2nd Eu). McGraw Hill.
Hilscher, J., & Wilson, M. (2016). Credit ratings and credit risk: Is one measure enough?. Management science, 63(10), 3414-3437.
Hu, H., & Sathye, M. (2015). Predicting financial distress in the Hong Kong growth enterprises market from the perspective of financial sustainability. Sustainability, 7(2), 1186-1200.
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.
Jones, S., & Hensher, D. A. (2004). Predicting firm financial distress: A mixed logit model. The Accounting Review, 79(4), 1011-1038.
Koh, H. C., & Tan, S. S. (1999). A neural network approach to the prediction of going concern status. Accounting and Business Research, 29(3), 211-216.
Lau, A. H. L. (1987). A five-state financial distress prediction model. Journal of accounting research, 127-138.
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.
Low, S. W., Fauzias, M. N., & Yatim, P. (2001). Predicting corporate financial distress using the logit model: The case of Malaysia. Asian Academy of Management Journal, 6(1), 49-61.
Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29(2), 449-470.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 109-131.
Santoso, N., & Wibowo, W. (2018, March). Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine. In Journal of Physics: Conference Series (Vol. 979, No. 1, p. 012089). IOP Publishing.
Sun, J., Li, H., Huang, Q. H., & He, K. Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56.
Wruck, K. H. (1990). Financial distress, reorganization, and organizational efficiency. Journal of financial economics, 27(2), 419-444.
Xie, C., Luo, C., & Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies. Quality & Quantity, 45(3), 671-686.
Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firms: a logistic analysis. Journal of Business Finance & Accounting, 12(1), 19-45.
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting research, 59-82.
描述 碩士
國立政治大學
會計學系
106353020
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106353020
資料類型 thesis
dc.contributor.advisor 林宛瑩zh_TW
dc.contributor.author (Authors) 林霈吾zh_TW
dc.contributor.author (Authors) Lin, Pei-Wuen_US
dc.creator (作者) 林霈吾zh_TW
dc.creator (作者) Lin, Pei-Wuen_US
dc.date (日期) 2019en_US
dc.date.accessioned 5-Sep-2019 15:40:20 (UTC+8)-
dc.date.available 5-Sep-2019 15:40:20 (UTC+8)-
dc.date.issued (上傳時間) 5-Sep-2019 15:40:20 (UTC+8)-
dc.identifier (Other Identifiers) G0106353020en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125507-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 會計學系zh_TW
dc.description (描述) 106353020zh_TW
dc.description.abstract (摘要) 近十年的快速成長吸引了各類國際企業與投資機構進入中國市場，然而由於法制的不成熟及國家體制的特殊，舞弊層出不窮。在此背景下，香港轉而成為眾多外資的首選，然而國內外資金的流入帶來繁榮也為香港市場帶來未知數，市場對信用風險預警制度的需求日漸提高，至今卻沒有太多相關之研究。為了瞭解影響香港企業信用風險之因素，本文以2008年到2017年間的香港主板上市公司為研究對象，參考過去文獻與香港市場現況為危機事件定義，再透過邏輯迴歸、常態機率迴歸及多元判別分析進行實證研究，分別以流動性、獲利性及安全性三個面向探討有關之解釋變數，研究結果顯示多元辦別分析模型在特定組合下於危機事件發生前一年有高達66.25%的危機樣本預測準確率，而於危機事件前兩年之危機樣本預測效果則僅不到1%的下降。zh_TW
dc.description.abstract (摘要) Due to the high-speed of growth in China, the hot money of foreign investors pours in and the China stock market become popular in Asian. However, the weakness in laws and regulations make it risky to invest in this market. In contrast to China, the stable financial environment and relatively sound legal systems make Hong Kong a better choice for foreign companies and investors. These cash flows bring prosperity to Hong Kong, and increase the instability and the needs of credit risk prediction model in the whole market at the same time. In order to realize the factors of financial distress for Hong Kong listed company, I select some listed company during 2008-2017 as the samples. By taking the financial environment into account, I determine the definitions of distress. Then, I use logistic regression model, probit model and multiple discriminant analysis model to test the three different kinds of variables and figure out which are significant to distress prediction. The result shows that the accuracy of multiple discriminant analysis model in distress company can up to 66.25 percent.en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究議題 4
第三節 論文架構 5
第二章 文獻探討 6
第一節 信用風險預警相關研究 6
第二節 危機事件定義之探討 12
第三章 研究方法 14
第一節 研究假說 14
第二節 實證模型與變數定義 14
第三節 樣本選取與抽樣 23
第四章 實證結果與分析 24
第一節 敘述性統計 24
第二節 相關係數矩陣分析 26
第三節 實證模型分析結果 29
第四節 模型預測結果分析 37
第五章 結論與建議 41
第一節 結論 41
第二節 研究限制與建議 42
附錄 43
參考文獻 50		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106353020en_US
dc.subject (關鍵詞) 信用風險預警zh_TW
dc.subject (關鍵詞) 企業失敗zh_TW
dc.subject (關鍵詞) 邏輯迴歸分析zh_TW
dc.subject (關鍵詞) 多元判別分析zh_TW
dc.subject (關鍵詞) credit risk predictionen_US
dc.subject (關鍵詞) financial distressen_US
dc.subject (關鍵詞) logistic regression analysisen_US
dc.subject (關鍵詞) multiple discriminant analysisen_US
dc.title (題名) 香港企業信用風險預警制度之建構與研究zh_TW
dc.title (題名) Research of Credit Risk Prediction in Hong Kongen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 牛陳才，2011，我國上市公司財務預警的實證分析——基於修正的 Z-score 模型，中國管理資訊化，23期，11-14。
周百隆與盧俊安，2007，以Cascaded Logistic Model 建構我國企業財務危機預警模型之研究，中華管理評論國際學報，第十卷，第2期。
林宓穎，2002，上市公司財務危機預警模式之研究，國立政治大學財政學系碩士論文。
林郁翎與黃建華，2009，考慮公司治理之企業財務危機預警模型。東吳經濟商學學報，(64)，23-55。
郑茂，2003，我国上市公司财务风险预警模型的构建及实证分析，金融论坛，8(10)，38-42。
黃華山與邱一薰，2005，類神經網路預測台灣50股價指數之研究，資訊、科技與社會學報，第9期，19-42。
潘春健，2006，香港創業板上市公司財務預警實證分析，嘉興學院學報，第1期，66-69。
蔡孟哲與許溪南，2018，應用類神經網路建構台灣上市上櫃公司 TCRI 信用風險模式，TANET2018 臺灣網際網路研討會，1134-1139。
盧嘉梧與林志軒，2016，類神經網路投資組合策略績效之實證研究: 以台灣中型100電子股為例，輔仁管理評論，23(3)，29-50。
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.
Altman, E. I., & Hotchkiss, E. (1993). Corporate financial distress and bankruptcy.
Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis A new model to identify bankruptcy risk of corporations. Journal of banking & finance, 1(1), 29-54.
Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of banking & finance, 18(3), 505-529.
Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111.
Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of political economy, 81(3), 637-654.
Blum, M. (1974). Failing company discriminant analysis. Journal of accounting research, 1-25.
Brown, D. T., James, C. M., & Mooradian, R. M. (1994). Asset sales by financially distressed firms. Journal of Corporate finance, 1(2), 233-257.
Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899-2939.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of accounting research, 167-179.
Doumpos, M., & Zopounidis, C. (1999). A multicriteria discrimination method for the prediction of financial distress: the case of Greece. Multinational Finance Journal, 3(2), 71-101.
Frydman, H., Altman, E. I., & Kao, D. L. (1985). Introducing recursive partitioning for financial classification: the case of financial distress. The Journal of Finance, 40(1), 269-291.
Gilson, S. C., John, K., & Lang, L. H. (1990). Troubled debt restructurings: An empirical study of private reorganization of firms in default. Journal of financial economics, 27(2), 315-353.
Hillier, D., Ross, S., Westerfield, R., Jaffe, J., & Jordan, B. (2013). Corporate finance (No. 2nd Eu). McGraw Hill.
Hilscher, J., & Wilson, M. (2016). Credit ratings and credit risk: Is one measure enough?. Management science, 63(10), 3414-3437.
Hu, H., & Sathye, M. (2015). Predicting financial distress in the Hong Kong growth enterprises market from the perspective of financial sustainability. Sustainability, 7(2), 1186-1200.
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.
Jones, S., & Hensher, D. A. (2004). Predicting firm financial distress: A mixed logit model. The Accounting Review, 79(4), 1011-1038.
Koh, H. C., & Tan, S. S. (1999). A neural network approach to the prediction of going concern status. Accounting and Business Research, 29(3), 211-216.
Lau, A. H. L. (1987). A five-state financial distress prediction model. Journal of accounting research, 127-138.
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.
Low, S. W., Fauzias, M. N., & Yatim, P. (2001). Predicting corporate financial distress using the logit model: The case of Malaysia. Asian Academy of Management Journal, 6(1), 49-61.
Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29(2), 449-470.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 109-131.
Santoso, N., & Wibowo, W. (2018, March). Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine. In Journal of Physics: Conference Series (Vol. 979, No. 1, p. 012089). IOP Publishing.
Sun, J., Li, H., Huang, Q. H., & He, K. Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56.
Wruck, K. H. (1990). Financial distress, reorganization, and organizational efficiency. Journal of financial economics, 27(2), 419-444.
Xie, C., Luo, C., & Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies. Quality & Quantity, 45(3), 671-686.
Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firms: a logistic analysis. Journal of Business Finance & Accounting, 12(1), 19-45.
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting research, 59-82.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU201900968en_US