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題名 產險公司破產預測之分析：運用新類神經網路方法
Solvency Prediction of Property-Casualty Insurance Company - A New Neural Network Approach
作者 魏佑珊
Wei, Yu Shan
貢獻者 蔡政憲
魏佑珊
Wei, Yu Shan
關鍵詞 產險業
清償能力
類神經網路
Property and casualty insurers
Insolvency
Neural network
日期 2002
上傳時間 9-May-2016 16:31:03 (UTC+8)
摘要 　　保險業的清償能力一直是保險監理機關關心的重點，保險公司一旦失卻清償能力，所影響的將不只是該公司，還有龐大的保戶及社會大眾。自西元1988年開始，即有許多學者提出早期預警模型，針對保險公司的清償能力作預測，希望可以及早發覺問題保險公司，直到西元1994年，開始有學者以類神經網路作為預測工具，結果發現，其預測準確度較過去多篇文獻所認為的邏輯斯迴歸來的精確。
　　本論文的目的在利用新的類神經網路建構保險公司失卻清償能力的早期預警系統，並將其結果與邏輯斯迴歸之結果作比較，樣本為美國產險公司，實證結果顯示，若以類神經網路作為預測的工具，在預測破產公司方面，其結果較邏輯斯迴歸好；但若是在預測健全公司方面，則為邏輯斯迴歸較好。另外，就整體的預測準確度而言，則以類神經網路的預測結果較好。
　　The solvency of insurance industry plays an important role in society and has been the focus of insurance regulation. The insurer insolvency will affect not only company itself, but also the policyholders and society. The better method up to 1994 to identify insurer insolvencies in most prior researches is logistic regression. Some scholars use neural networks to predict insurer insolvencies. The result showed that neural network performed better than logistic regression model.
　　The purpose of this paper aims to construct an early warning system for property and casualty insurer insolvencies prediction and to compare the predictive ability of neural network and logistic regression model. The results show that neural network performs better than logistic regression model in classifying insolvent insurers. On contrast, logistic regression model performs better in classifying solvent insurers. Overall, the neural network performs better than its counterpart based on all sample firms.
參考文獻 宋瑞琳，「風險基礎資本，情境分析及變動模擬破產預測模型之比較」，政治大學風險管理與保險研究所碩士論文，民國九十年。
林建智、王儷玲，「美國保險業財務分析及清償能力追蹤系統之研究與建議」，財團法人保險事業發展中心，民國九十年。
Ambrose, J. M. and J. A. Seward, 1988, Best’s Ratings, Financial Ratios and Prior Probabilities in Insolvency Prediction, Journal of Risk and Insurance, 55: 229-244.
Ambrose, J. M. and A. M. Carroll, 1994, Using Best’s Ratings in life Insurer Insolvency Prediction, Journal of Risk and Insurance, 61: 317-327.
BarNiv, R. and A. Raveh, Summer 1989, Identifying Financial Distress: A New Nonparametric Approach, Journal of Business Finance and Accounting, 16: 361-383. Table 2.2 shows the final set of variables in the model.
BarNiv, R. and J. B. McDonald, 1992, Identifying Financial Distress in the Insurance Industry: A Synthesis of Methodological and Empirical Issues, Journal of Risk and Insurance, 59: 543-574.
BarNiv, R. and R. Hershbarger, 1990, Classifying Financial Distress in the Life Insurance Industry, Journal of Risk and Insurance, 57: 110-136.
Brockett, P. L., W. W. Cooper, L. L. Golden, and U. Pitaktong, 1994, A Neural Network Method for Obtaining An Early Warning of Insurer Insolvency, Journal of Risk and Insurance, 61: 402-424.
Browne, M. J., J. M. Carson, and R. E Hoyt, Dec. 1999, Economic and Market Predictors of Insolvencies in the Life-Health Insurance Industry, Journal of Risk and Insurance, 66: 643-659.
Browne, M. J., R. E Hoyt, Dec. 1995, Economic and Market Predictors of Insolvencies in the Property-Liability Insurance Industry, Journal of Risk and Insurance, 62: 309-327.
Carson, J. M., R. E. Hoyt, 1995, Life Insurer Financial Distress: Classification Models and Empirical Evidence, Journal of Risk and Insurance, 62: 764-775.
Chen, C. L., D. B. Kaber, and P. G. Dempsey, 2000, A New Approach to Applying Feedforward Neural network to the Prediction of Musculoskeletal Disorder Risk, Applied Ergonomics, 31: 269-282.
Cummins, J. D., M. F. Grace, and R. D. Phillips, Dec. 1999, Regulatory Solvency Prediction in Property-Liability Insurance: Risk-Based Capital, Audit Ratios, and Cash Flow Simulation, Journal of Risk and Insurance, 66: 417-458.
Grace, M. F., S, E. Harrington, and R. W. Klein, Jun. 1998, Risk-Based Capital and Solvency Screening in Property-Liability Insurance: Hypotheses and Empirical Issues, Journal of Risk and Insurance, 65: 213-243.
Huang, C. S, R. E. Dorsey, M. A. Boose, 1994, Life Insurer Financial Distress Prediction: A Neural Network Model, Journal of Insurance Regulation, 13: 131-167.
Lee, S. H., J. L. Urrutia, Mar. 1996, Analysis and Prediction of Insolvency in the Property-Liability Insurance Industry: A Comparison of Logit and Hazard Models, Journal of Risk and Insurance, 63: 121-130.
Pottier, S. W., Life Insurer Financial Distress, Best’s Ratings, and Financial Ratios Mar. 1998, Journal of Risk and Insurance, 65: 275-288.
Pottier, S. W. and D. W. Sommer, Dec. 1999, Property-Liability Insurer Financial Strength Ratings: Differences Across Rating Agencies, Journal of Risk and Insurance, 66: 621-642.
描述 碩士
國立政治大學
風險管理與保險研究所
89358007
資料來源 http://thesis.lib.nccu.edu.tw/record/#A2010000372
資料類型 thesis
dc.contributor.advisor 蔡政憲zh_TW
dc.contributor.author (Authors) 魏佑珊zh_TW
dc.contributor.author (Authors) Wei, Yu Shanen_US
dc.creator (作者) 魏佑珊zh_TW
dc.creator (作者) Wei, Yu Shanen_US
dc.date (日期) 2002en_US
dc.date.accessioned 9-May-2016 16:31:03 (UTC+8)-
dc.date.available 9-May-2016 16:31:03 (UTC+8)-
dc.date.issued (上傳時間) 9-May-2016 16:31:03 (UTC+8)-
dc.identifier (Other Identifiers) A2010000372en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/95560-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 風險管理與保險研究所zh_TW
dc.description (描述) 89358007zh_TW
dc.description.abstract (摘要) 　　保險業的清償能力一直是保險監理機關關心的重點，保險公司一旦失卻清償能力，所影響的將不只是該公司，還有龐大的保戶及社會大眾。自西元1988年開始，即有許多學者提出早期預警模型，針對保險公司的清償能力作預測，希望可以及早發覺問題保險公司，直到西元1994年，開始有學者以類神經網路作為預測工具，結果發現，其預測準確度較過去多篇文獻所認為的邏輯斯迴歸來的精確。
　　本論文的目的在利用新的類神經網路建構保險公司失卻清償能力的早期預警系統，並將其結果與邏輯斯迴歸之結果作比較，樣本為美國產險公司，實證結果顯示，若以類神經網路作為預測的工具，在預測破產公司方面，其結果較邏輯斯迴歸好；但若是在預測健全公司方面，則為邏輯斯迴歸較好。另外，就整體的預測準確度而言，則以類神經網路的預測結果較好。		zh_TW
dc.description.abstract (摘要) 　　The solvency of insurance industry plays an important role in society and has been the focus of insurance regulation. The insurer insolvency will affect not only company itself, but also the policyholders and society. The better method up to 1994 to identify insurer insolvencies in most prior researches is logistic regression. Some scholars use neural networks to predict insurer insolvencies. The result showed that neural network performed better than logistic regression model.
　　The purpose of this paper aims to construct an early warning system for property and casualty insurer insolvencies prediction and to compare the predictive ability of neural network and logistic regression model. The results show that neural network performs better than logistic regression model in classifying insolvent insurers. On contrast, logistic regression model performs better in classifying solvent insurers. Overall, the neural network performs better than its counterpart based on all sample firms.		en_US
dc.description.tableofcontents 謝辭
摘要
Abstract
Table of Content
1. Introduction-----1
2. Neural Network-----5
3. Variable Selection-----9
4. Sample Selection and Empirical Result-----10
5. Conclusion-----14
Figures & Tables
Reference		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#A2010000372en_US
dc.subject (關鍵詞) 產險業zh_TW
dc.subject (關鍵詞) 清償能力zh_TW
dc.subject (關鍵詞) 類神經網路zh_TW
dc.subject (關鍵詞) Property and casualty insurersen_US
dc.subject (關鍵詞) Insolvencyen_US
dc.subject (關鍵詞) Neural networken_US
dc.title (題名) 產險公司破產預測之分析：運用新類神經網路方法zh_TW
dc.title (題名) Solvency Prediction of Property-Casualty Insurance Company - A New Neural Network Approachen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 宋瑞琳，「風險基礎資本，情境分析及變動模擬破產預測模型之比較」，政治大學風險管理與保險研究所碩士論文，民國九十年。
林建智、王儷玲，「美國保險業財務分析及清償能力追蹤系統之研究與建議」，財團法人保險事業發展中心，民國九十年。
Ambrose, J. M. and J. A. Seward, 1988, Best’s Ratings, Financial Ratios and Prior Probabilities in Insolvency Prediction, Journal of Risk and Insurance, 55: 229-244.
Ambrose, J. M. and A. M. Carroll, 1994, Using Best’s Ratings in life Insurer Insolvency Prediction, Journal of Risk and Insurance, 61: 317-327.
BarNiv, R. and A. Raveh, Summer 1989, Identifying Financial Distress: A New Nonparametric Approach, Journal of Business Finance and Accounting, 16: 361-383. Table 2.2 shows the final set of variables in the model.
BarNiv, R. and J. B. McDonald, 1992, Identifying Financial Distress in the Insurance Industry: A Synthesis of Methodological and Empirical Issues, Journal of Risk and Insurance, 59: 543-574.
BarNiv, R. and R. Hershbarger, 1990, Classifying Financial Distress in the Life Insurance Industry, Journal of Risk and Insurance, 57: 110-136.
Brockett, P. L., W. W. Cooper, L. L. Golden, and U. Pitaktong, 1994, A Neural Network Method for Obtaining An Early Warning of Insurer Insolvency, Journal of Risk and Insurance, 61: 402-424.
Browne, M. J., J. M. Carson, and R. E Hoyt, Dec. 1999, Economic and Market Predictors of Insolvencies in the Life-Health Insurance Industry, Journal of Risk and Insurance, 66: 643-659.
Browne, M. J., R. E Hoyt, Dec. 1995, Economic and Market Predictors of Insolvencies in the Property-Liability Insurance Industry, Journal of Risk and Insurance, 62: 309-327.
Carson, J. M., R. E. Hoyt, 1995, Life Insurer Financial Distress: Classification Models and Empirical Evidence, Journal of Risk and Insurance, 62: 764-775.
Chen, C. L., D. B. Kaber, and P. G. Dempsey, 2000, A New Approach to Applying Feedforward Neural network to the Prediction of Musculoskeletal Disorder Risk, Applied Ergonomics, 31: 269-282.
Cummins, J. D., M. F. Grace, and R. D. Phillips, Dec. 1999, Regulatory Solvency Prediction in Property-Liability Insurance: Risk-Based Capital, Audit Ratios, and Cash Flow Simulation, Journal of Risk and Insurance, 66: 417-458.
Grace, M. F., S, E. Harrington, and R. W. Klein, Jun. 1998, Risk-Based Capital and Solvency Screening in Property-Liability Insurance: Hypotheses and Empirical Issues, Journal of Risk and Insurance, 65: 213-243.
Huang, C. S, R. E. Dorsey, M. A. Boose, 1994, Life Insurer Financial Distress Prediction: A Neural Network Model, Journal of Insurance Regulation, 13: 131-167.
Lee, S. H., J. L. Urrutia, Mar. 1996, Analysis and Prediction of Insolvency in the Property-Liability Insurance Industry: A Comparison of Logit and Hazard Models, Journal of Risk and Insurance, 63: 121-130.
Pottier, S. W., Life Insurer Financial Distress, Best’s Ratings, and Financial Ratios Mar. 1998, Journal of Risk and Insurance, 65: 275-288.
Pottier, S. W. and D. W. Sommer, Dec. 1999, Property-Liability Insurer Financial Strength Ratings: Differences Across Rating Agencies, Journal of Risk and Insurance, 66: 621-642.		zh_TW