演化式类神经网络评估信息评鉴系统对预测财务危机的影响.doc
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演化式类神经网络评估信息评鉴系统对预测财务危机的影响,摘 要本研究旨在以类神经网络结合信息揭露评鉴系统对公司进行财务危机预测，探讨政府推动信息揭露评鉴系统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财务危机预测的准确性。以传统罗吉斯回归作为倒传递类神经网络（bpn）与演化式类神经网络(enn)之财务危机预测模...
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演化式类神经网络评估信息评鉴系统对预测财务危机的影响
摘 要
本研究旨在以类神经网络结合信息揭露评鉴系统对公司进行财务危机预测，探讨政府推动信息揭露评鉴系统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财务危机预测的准确性。以传统罗吉斯回归作为倒传递类神经网络（BPN）与演化式类神经网络(ENN)之财务危机预测模型预测能力的比较基准。实证结果发现信息揭露程度可增加财务危机预测的准确性，亦即信息揭露评鉴系统具备有用性；以预测准确性而言，演化式类神经网络模型优于倒传递类神经网络模型优于罗吉斯回归模型，因此，应优先采用演化式类神经网络来建构财务危机预测模型。本研究结果希能有助于财务报表使用者进行正确的投资决策，再者，供管制机关推动公司治理制度，促使强化信息公开机制之依据。
关键词：信息揭露、公司治理、财务危机、倒传递类神经网络、遗传算法
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
参考文献
李昭慧，2007，基因算法与决策树于企业财务危机预警之研究，佛光大学信息学系研究所未出版硕士论文。
吴当杰，2007，公司治理理论与实务，第二版，财团法人中华民国证券暨期货市场发展基金会。
陈淑萍，2002，资料探勘应用于财务危机预警模式之研究，铭传大学信息管理研究所未出版硕士论文。
张斐章与张丽秋，2005，类神经网络，台湾东华书局股份有限公司。
叶怡成，2006，Super PCNeuron 5.0 类神经网络建构软件参考手册，中华大学信息管理学系 商业智慧研究室。
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
本研究旨在以类神经网络结合信息揭露评鉴系统对公司进行财务危机预测，探讨政府推动信息揭露评鉴系统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财务危机预测的准确性。以传统罗吉斯回归作为倒传递类神经网络（BPN）与演化式类神经网络(ENN)之财务危机预测模型预测能力的比较基准。实证结果发现信息揭露程度可增加财务危机预测的准确性，亦即信息揭露评鉴系统具备有用性；以预测准确性而言，演化式类神经网络模型优于倒传递类神经网络模型优于罗吉斯回归模型，因此，应优先采用演化式类神经网络来建构财务危机预测模型。本研究结果希能有助于财务报表使用者进行正确的投资决策，再者，供管制机关推动公司治理制度，促使强化信息公开机制之依据。
关键词：信息揭露、公司治理、财务危机、倒传递类神经网络、遗传算法
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
参考文献
李昭慧，2007，基因算法与决策树于企业财务危机预警之研究，佛光大学信息学系研究所未出版硕士论文。
吴当杰，2007，公司治理理论与实务，第二版，财团法人中华民国证券暨期货市场发展基金会。
陈淑萍，2002，资料探勘应用于财务危机预警模式之研究，铭传大学信息管理研究所未出版硕士论文。
张斐章与张丽秋，2005，类神经网络，台湾东华书局股份有限公司。
叶怡成，2006，Super PCNeuron 5.0 类神经网络建构软件参考手册，中华大学信息管理学系 商业智慧研究室。
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.
