演化式類神經(jīng)網(wǎng)絡評估信息評鑒系統(tǒng)對預測財務危機的影響.doc
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演化式類神經(jīng)網(wǎng)絡評估信息評鑒系統(tǒng)對預測財務危機的影響,摘 要本研究旨在以類神經(jīng)網(wǎng)絡結合信息揭露評鑒系統(tǒng)對公司進行財務危機預測，探討政府推動信息揭露評鑒系統(tǒng)是否真能提升公司信息透明度、健全公司治理制度以及增加公司財務危機預測的準確性。以傳統(tǒng)羅吉斯回歸作為倒傳遞類神經(jīng)網(wǎng)絡（bpn）與演化式類神經(jīng)網(wǎng)絡(enn)之財務危機預測模...
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演化式類神經(jīng)網(wǎng)絡評估信息評鑒系統(tǒng)對預測財務危機的影響
摘 要
本研究旨在以類神經(jīng)網(wǎng)絡結合信息揭露評鑒系統(tǒng)對公司進行財務危機預測，探討政府推動信息揭露評鑒系統(tǒng)是否真能提升公司信息透明度、健全公司治理制度以及增加公司財務危機預測的準確性。以傳統(tǒng)羅吉斯回歸作為倒傳遞類神經(jīng)網(wǎng)絡（BPN）與演化式類神經(jīng)網(wǎng)絡(ENN)之財務危機預測模型預測能力的比較基準。實證結果發(fā)現(xiàn)信息揭露程度可增加財務危機預測的準確性，亦即信息揭露評鑒系統(tǒng)具備有用性；以預測準確性而言，演化式類神經(jīng)網(wǎng)絡模型優(yōu)于倒傳遞類神經(jīng)網(wǎng)絡模型優(yōu)于羅吉斯回歸模型，因此，應優(yōu)先采用演化式類神經(jīng)網(wǎng)絡來建構財務危機預測模型。本研究結果希能有助于財務報表使用者進行正確的投資決策，再者，供管制機關推動公司治理制度，促使強化信息公開機制之依據(jù)。
關鍵詞：信息揭露、公司治理、財務危機、倒傳遞類神經(jīng)網(wǎng)絡、遺傳算法
The Neural Network and Information Disclosure System to the Prediction of Financial Distress status
Abstract
The main purpose of this study is to construct back propagation neural network (BPN) and evolutionary neural network (ENN) based on information disclosure system. This paper investigates the usefulness of information disclosure system. It is find that Information disclosure system can not only increase information transparency but also improve preciseness of financial distress prediction. Compared with the traditional used logit model, it can discover that back propagation neural network and evolutionary neural network can provide more accurate prediction and information value. According preciseness of prediction, evolutionary neural network is better than back propagation neural network. Thus, adaptation genetic algorithms on neural network to construct financial distress prediction model is the best choice. Besides, this research’s result can not only provide users of financial statement to make good decision of investment but also help regulator practiced corporate governance mechanism and enhance information disclosure system.
Keywords: Information Disclosure, Corporate governance, Financial Distress, Back Propagation Neural network, Genetic Algorithms
參考文獻
李昭慧，2007，基因算法與決策樹于企業(yè)財務危機預警之研究，佛光大學信息學系研究所未出版碩士論文。
吳當杰，2007，公司治理理論與實務，第二版，財團法人中華民國證券暨期貨市場發(fā)展基金會。
陳淑萍，2002，資料探勘應用于財務危機預警模式之研究，銘傳大學信息管理研究所未出版碩士論文。
張斐章與張麗秋，2005，類神經(jīng)網(wǎng)絡，臺灣東華書局股份有限公司。
葉怡成，2006，Super PCNeuron 5.0 類神經(jīng)網(wǎng)絡建構軟件參考手冊，中華大學信息管理學系 商業(yè)智慧研究室。
Altman, E. I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance. 23:589-609.
Beaver, W. H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research. 4:71-111.
Diamond, D. W. 1985. Optimal release of information by firms. Journal of Finance. 40:1071-1094.
Elliott, R. K. and P. D. Jacobson. 1994. Cost and benefits of business information disclosure. Accounting Horizons. 8:80-96.
Fernandez, E. and I. Olmeda. 1995. Bankruptcy prediction with artificial neural networks. Lect. Notes Comput. Sc. 1142-1146.
Holland, J. H. 1975. Adaptation in natural and artificial systems. University of Michigan, Cambridge, MIT Press, MA.
Jensen, M. C., and W. H. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Econmics. 13:305-360.
Koh, H. C., and S. S. Tan. 1999. A neural network approach to the prediction of going concern status. Accounting and Business Research. 29:211-216.
Lori, Holder-Webb. 2003. Strategic use of disclosure policy in distressed firms. Woring paper. University of Wisconsin-Madison.
McCulloch W. S. and W. Pitts. 1943. A logical Calculus of the Ideas Immanent in Nervous Activity. Bullentin of Mathematical Biophysics. 5:115-133.
Miller, G. S. 2002. Earnings performance and discretionary disclosure. Journal of Accounting Research. 40:173-204.
Odom, M. D. and R. Sharda. 1990. A neural network model for bankruptcy prediction. Proceedings of the IEEE International Conference on Neural Network. 2:163-168.
Ohlson, J. A. 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research. 18:109-131.
Smith, R. F. and A. H. Winakor. 1935. Changes in financial structure of unsuccessful industrial companies. Bureau of Business Research. University of Illinois.
Sung, T. K., N. Chang and G. Lee. 1999. Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems. 16:63-85.
Tam, K. Y. and M. Y. Kiang. 1992. Managerial applications of neural networks: The case of bank failure predictions. Management Science. 38:926-947.
Zurada, J. M. 1992. Introduction to Artificial Neural Systems. St. Paul, MN: West Publishing.
Zwijewski, M. E. 1984. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research. 22:59-82.
摘 要
本研究旨在以類神經(jīng)網(wǎng)絡結合信息揭露評鑒系統(tǒng)對公司進行財務危機預測，探討政府推動信息揭露評鑒系統(tǒng)是否真能提升公司信息透明度、健全公司治理制度以及增加公司財務危機預測的準確性。以傳統(tǒng)羅吉斯回歸作為倒傳遞類神經(jīng)網(wǎng)絡（BPN）與演化式類神經(jīng)網(wǎng)絡(ENN)之財務危機預測模型預測能力的比較基準。實證結果發(fā)現(xiàn)信息揭露程度可增加財務危機預測的準確性，亦即信息揭露評鑒系統(tǒng)具備有用性；以預測準確性而言，演化式類神經(jīng)網(wǎng)絡模型優(yōu)于倒傳遞類神經(jīng)網(wǎng)絡模型優(yōu)于羅吉斯回歸模型，因此，應優(yōu)先采用演化式類神經(jīng)網(wǎng)絡來建構財務危機預測模型。本研究結果希能有助于財務報表使用者進行正確的投資決策，再者，供管制機關推動公司治理制度，促使強化信息公開機制之依據(jù)。
關鍵詞：信息揭露、公司治理、財務危機、倒傳遞類神經(jīng)網(wǎng)絡、遺傳算法
The Neural Network and Information Disclosure System to the Prediction of Financial Distress status
Abstract
The main purpose of this study is to construct back propagation neural network (BPN) and evolutionary neural network (ENN) based on information disclosure system. This paper investigates the usefulness of information disclosure system. It is find that Information disclosure system can not only increase information transparency but also improve preciseness of financial distress prediction. Compared with the traditional used logit model, it can discover that back propagation neural network and evolutionary neural network can provide more accurate prediction and information value. According preciseness of prediction, evolutionary neural network is better than back propagation neural network. Thus, adaptation genetic algorithms on neural network to construct financial distress prediction model is the best choice. Besides, this research’s result can not only provide users of financial statement to make good decision of investment but also help regulator practiced corporate governance mechanism and enhance information disclosure system.
Keywords: Information Disclosure, Corporate governance, Financial Distress, Back Propagation Neural network, Genetic Algorithms
參考文獻
李昭慧，2007，基因算法與決策樹于企業(yè)財務危機預警之研究，佛光大學信息學系研究所未出版碩士論文。
吳當杰，2007，公司治理理論與實務，第二版，財團法人中華民國證券暨期貨市場發(fā)展基金會。
陳淑萍，2002，資料探勘應用于財務危機預警模式之研究，銘傳大學信息管理研究所未出版碩士論文。
張斐章與張麗秋，2005，類神經(jīng)網(wǎng)絡，臺灣東華書局股份有限公司。
葉怡成，2006，Super PCNeuron 5.0 類神經(jīng)網(wǎng)絡建構軟件參考手冊，中華大學信息管理學系 商業(yè)智慧研究室。
Altman, E. I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance. 23:589-609.
Beaver, W. H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research. 4:71-111.
Diamond, D. W. 1985. Optimal release of information by firms. Journal of Finance. 40:1071-1094.
Elliott, R. K. and P. D. Jacobson. 1994. Cost and benefits of business information disclosure. Accounting Horizons. 8:80-96.
Fernandez, E. and I. Olmeda. 1995. Bankruptcy prediction with artificial neural networks. Lect. Notes Comput. Sc. 1142-1146.
Holland, J. H. 1975. Adaptation in natural and artificial systems. University of Michigan, Cambridge, MIT Press, MA.
Jensen, M. C., and W. H. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Econmics. 13:305-360.
Koh, H. C., and S. S. Tan. 1999. A neural network approach to the prediction of going concern status. Accounting and Business Research. 29:211-216.
Lori, Holder-Webb. 2003. Strategic use of disclosure policy in distressed firms. Woring paper. University of Wisconsin-Madison.
McCulloch W. S. and W. Pitts. 1943. A logical Calculus of the Ideas Immanent in Nervous Activity. Bullentin of Mathematical Biophysics. 5:115-133.
Miller, G. S. 2002. Earnings performance and discretionary disclosure. Journal of Accounting Research. 40:173-204.
Odom, M. D. and R. Sharda. 1990. A neural network model for bankruptcy prediction. Proceedings of the IEEE International Conference on Neural Network. 2:163-168.
Ohlson, J. A. 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research. 18:109-131.
Smith, R. F. and A. H. Winakor. 1935. Changes in financial structure of unsuccessful industrial companies. Bureau of Business Research. University of Illinois.
Sung, T. K., N. Chang and G. Lee. 1999. Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems. 16:63-85.
Tam, K. Y. and M. Y. Kiang. 1992. Managerial applications of neural networks: The case of bank failure predictions. Management Science. 38:926-947.
Zurada, J. M. 1992. Introduction to Artificial Neural Systems. St. Paul, MN: West Publishing.
Zwijewski, M. E. 1984. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research. 22:59-82.
