Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/20664
DC FieldValueLanguage
dc.contributor陳家彬zh_TW
dc.contributorChia-Pin Chenen_US
dc.contributor蕭子誼zh_TW
dc.contributor許光華zh_TW
dc.contributorTzy-Yih Hsiaoen_US
dc.contributorKuang-Hua Hsuen_US
dc.contributor.advisor林金賢zh_TW
dc.contributor.advisorChin-Shien Linen_US
dc.contributor.author黃堂軒zh_TW
dc.contributor.authorHuang, Tang-Hsuanen_US
dc.contributor.other中興大學zh_TW
dc.date2008zh_TW
dc.date.accessioned2014-06-06T07:14:12Z-
dc.date.available2014-06-06T07:14:12Z-
dc.identifierU0005-2706200701522700zh_TW
dc.identifier.citation中文部分 林金賢、劉沂佩、鄭育書、陳育成 (民93)。具學習性之模糊專家系統在財務危機預測上之應用。管理學報,21 (3),291-309。 林郁翎 (民91)。銀行危機預警綜合指標之建立—Signal Extraction Approach與Panel Logit Model 之結合。未出版之碩士論文,東吳大學經濟學研究所,台北市。 郭芳宜 (民92)。訊號分析法於企業財務預警之應用。未出版之碩士論文,靜宜大學企業管理研究所,台中縣。 陳耀茂 (民93)。類神經網路PC Neuron使用手冊。臺北市:鼎茂圖書出版股份有限公司。 葉怡成 (民92)。類神經網路模式應用與實作。臺北市:儒林圖書有限公司。 葉銀華、李存修 (民91)。整合公司治理、會計資訊與總體經濟敏感度之財務危機模型(行政院國家科學委員會專題研究計畫成果報告,NSC90-2416-H-030-004)。台北縣:輔仁大學國際貿易與金融學系。 魏曉琴 (民93)。財務危機預警模型之研究—以台灣地區上市公司為例。未出版之碩士論文,國立交通大學財務金融研究所,新竹市。 英文部分 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., Haldeman, R. G., and Narayanan, P. (1977). ZETA analysis: a new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29–51. Altrock, C. V. (1996). Fuzzy logic and neuron fuzzy applications in business and finance. New Jersey: Prentice-Hall Inc. Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Transactions on Neural Networks, 12(4), 929–935. Balcaen, S., and Ooghe, H. (2004). Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods, Working Paper. Balcaen, S., and 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, 63-93. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research , 4, 71–111. Chiang, W. C., Urban, T. L., and Baldridge, G. W. (1996). A neural network approach to mutual fund net asset value forecasting. Omega: International Journal of Management Sciences, 24, 205-215. Chirinko, R. S., Guill, G. D., and Hebert, P. (1991). Developing a systematic approach to credit risk management. Journal of Retail Banking, 13(3), 29-37. Cox, D. R. (1972). Regression models and life-tables. Journal of the royal statistical society, B 34, 187-220. Crapp, H., and Stevenson, M. (1987). Development of a method to assess the relevant variables and the probability of financial distress. Australian Journal of Management, 12(2), 221-236. Dimitras, A., Zanakis, S., and Zopoudinis, C. (1996). A survey of business failures with an emphasis on failure prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487–513. Dirickx, Y., and Van Landeghem, G. (1994). Statistical failure prevision problems. Tijdschrift voor Economie en Management, 39(4), 429–462. Eisenbeis, R. A. (1977). Pitfalls in the application of discriminant analysis in business. Journal of Finance, 32(3), 875–900. Fernandez-Rodriguez, F., Gonzalez-Martel, C., and Sosvilla-Rivero, S. (2000). On the profitability of technical trading rules based on artifical neural networks: evidence from the madrid stock market. Economics Letters, 69, 89-94. Foster, B., Collopy, F., and Ungar, L. (1992). Neural network forecasting of short, noisy time series. Computers and Chemical Engineering, 16(12), 293-297. Gessner, G., Kamakura, W. A., Malhortra, N. K., and Zmijewski, M. E. (1988). Estimating models with binary dependent variables: some theoretical and empirical observations. Journal of Business Research, 16(1), 49-65. Gloubos, G.., and Grammatikos, T. (1988). The success of bankruptcy prediction models in Greece. Studies in Banking and Finance, 7, 37–46. Hawley, D. D., Johnson, J. D., and Raina, D. (1990). Artificial neural systems: A new tool for financial decision-making. Financial Analysts Journal, 46(6), 63-72. Hsieh, S. (1993). A note on the optimal cutoff point in bankruptcy prediction models. Journal of Business Finance & Accounting, 20(3), 457-464. Kaminsky, G. L., and Reinhart, C. M. (1999). The twin crises: the causes of banking and balance-of-payments problems. The American Economic Review, 89, 473-500. Earlier version issued as Board of Governors International Finance Discussion Paper 544 (March 1996). Kaminsky, G. L., Lizondo, S., and Reinhart, C. M. (1998). Leading indicators of currency crises. IMF Staff Papers, 45, 1-48. Kleinbaum, D. G. (1997). Survival analysis: a self-learning text. Springer-Verlag, New York. Laitinen, E. K. (2005). Survival analysis and financial distress prediction: Finnish evidence. Review of Accounting and Finance, 4(4), 76-90. Lane, W. R., Looney, S. W., and Wansley, J. W. (1986). An application of the Cox proportional hazards model to bank failure. Journal of Banking and Finance, 10, 511–531. Luoma, M., and Laitinen, E. K. (1991). Survival analysis as a tool for company failure prediction. Omega: International Journal of Management Science, 19(6), 673-678. Mckim, R. A. (1993). Neural network applications to cost engineering. Cost Engineering, 35, 31-35. Mose, D., and Liao, S. S. (1987). On developing models for failure prediction. Journal of Commercial Bank Lending, 69, 27–38. Odom, M., and Sharda, R. (1990). Bankruptcy prediction using neural networks. Proceedings of the IEEE International Conference on Neural Networks, San Diego, 133–168. Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. Ooghe, H., and Balcaen, S. (2002). Are failure prediction models transferable from one country to another? An empirical study using Belgian financial statements. Proceedings of the 9th Annual Conference of the Multinational Finance Society, 30/06/02-03/07/02, Cyprus. Ooghe, H., Camerlynck, J., and Balcaen, S. (2003). The Ooghe-Joos-De Vos failure prediction models: a cross-industry validation. Brussels Economic Review, 46(1), 39-70. Shachmurove, Y. (2002). Applying artificial neural networks to business. economics and finance. Working paper 02-08, Center for Analytic Research in Economics and the Social Sciences (CARESS), University of Pennsylvania, USA, 1-47. Shumway, T. (2001). Forecasting bankruptcy more accurately: a simple hazard model. The Journal of Business, 74(1), 101–124. Tam, K. Y., and Kiang, M. Y. (1992). Managerial applications of neural networks: The Case of Bank Failure Predictions. Management of Science, 38(7), 926-947. Tamari, M. (1966). Financial ratios as a means of forecasting bankruptcy. Management International Review, 4, 15–21. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353. Zavgren, C. V. (1983). The prediction of corporate failure: the state of the art. Journal of Accounting Literature, 2, 1–38. Zavgren, C.V. (1985). Assessing the vulnerability to failure of American industrial firms: a logistic analysis. Journal of Business Finance and Accounting, 12(1), 19-45. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22 (1), 59–86.zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/20664-
dc.description.abstractOver forty years of development in the Finance literature, scholars have researched on issues concerning financial crisis forecasting. The researches could be classified into two groups. The first group incorporates different types of forecasting variables to improve the accuracy of financial crisis forecasting models, such as corporate governance variables and macroeconomic sensitivities. The second group uses different analytical methods to formulate financial crisis model in order to improve the model's forecasting ability In terms of analytical tools, the main focus of previous literature is using different analytical tools to improve the model's predictibility and performance. However, a gap exists in the literature in which differences in analytical tools have not been addressed and compared to one another when applying to financial crisis forcasteing related issues, such as choices of research sample, neglecting the influences of time. Hence, this research uses twenty years of financial informations of Taiwan's firms and employ six analytical tools (multivariate discriminant analysis, logit regression, neural network, neuro fuzzy, survival analysis, and signal approach) to formulate the financial crisis forecasting model in order to compare the differences in usability and applicability related issues of analytical tools. Empirical evidences show that in the short run, all the analytical tools exibit sound performance. Moreover, as the time period increases between the forecasting point and the crisis, artificial intelligence have a better performance than other tools. Using logitudinal analysis not only can solve the problem of sampling biase, but it can also observe the dynamic process of the financial crisis. Lastly, this research discusses the applicability of different analytical tools under different requirements and circumstances.en_US
dc.description.abstract經過四十年的發展,學界發表了許多有關財務危機預測議題的文獻,其中大致可分為兩類:一為藉由增加不同類型的預測變數來改善財務危機預測模型的準確度,例如公司治理變數、總體經濟敏感度等,另一類則以不同分析方法建構財務危機模型,來提高模型的預測能力。 在分析方法方面,過去文獻多著重以不同方析工具來改善模型的預測能力與績效,缺乏相關文獻去釐清分析工具之間的差異性,並廣泛地比較各種不同分析方法在財務危機預測的應用與其相關問題,例如:樣本的選擇、忽略時間構面的影響等問題。因此,本研究以台灣上市公司二十年的財務資訊,並採用六種分析方法 (多變量區別分析、羅吉斯迴歸、類神經網路、類神經模糊、存活分析法和訊號分析法) 來建構財務危機預測模型,藉此比較不同分析方法在財務危機預測問題上的使用性和相關問題。 實證結果顯示,短期的預測所有方法皆有不錯的表現,而隨著預測時間點距離發生危機的時間點愈長,人工智慧法的效益愈形顯現。此外,透過縱斷面分析法不但可以解決抽樣誤差,亦可以觀察財務危機發生之動態過程。最後,本研究討論不同分析方法在不同需求下適用之情形與表現,以供學術與實務界參考。zh_TW
dc.description.tableofcontents第一章 緒論1 第一節 研究背景1 第二節 研究問題與目的3 第三節 研究流程與架構4 第二章 文獻回顧與探討5 第一節 財務危機預測模型發展概況5 第二節 不同分析工具之文獻回顧9 第三章 研究方法19 第一節 研究樣本與變數19 第二節 分析方法25 第三節 績效比較39 第四章 實證分析42 第一節 模型的建立42 第二節 績效比較58 第五章 結論與建議71 第一節 研究結論與建議71 第二節 研究限制73 第三節 研究建議73 參考文獻74 附錄A 78 附錄B 79zh_TW
dc.language.isoen_USzh_TW
dc.publisher企業管理學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2706200701522700en_US
dc.subject財務危機zh_TW
dc.subjectfinancial crisisen_US
dc.subject類神經網路zh_TW
dc.subject類神經模糊zh_TW
dc.subject存活分析法zh_TW
dc.subject訊號分析法zh_TW
dc.subjectneural networken_US
dc.subjectneuro fuzzyen_US
dc.subjectsurvival analysisen_US
dc.subjectsignal approachen_US
dc.titleA Comparison of Different Methods in Forecasting Financial Crisisen_US
dc.title不同預測方法在財務危機問題的應用與比較zh_TW
dc.typeThesis and Dissertationzh_TW
Appears in Collections:企業管理學系所
文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.