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題名：A Comparison of Intelligent Techniques Accuracy for Financial Failure Prediction
書刊名：Academy of Taiwan Business Management Review
作者：Teoh, Hia Jong／Chu, Hsing-hui
出版日期：2015
卷期：11:2
頁次：頁34-43
主題關鍵詞：Financial failure prediction；Neural networks；Support vector machine；Rough Set Theory
原始連結：連回原系統網址new window
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Numerous researchers have used different intelligent techniques to predict financial failure, including Neural Networks, Support Vector Machines, and Rough Set Theory. All of them often fit data well, but the former two methods are not as comprehensible and transparent as the later one, in which they are often considered the black box techniques. The purpose of this paper is to compare the accuracy of Neural Networks, Support Vector Machine and Rough Set Theory by applying them to five financial failure datasets. The prediction accuracy results show that Rough Set Theory was relatively more accurate compared to Neural Networks and Support Vector Machines, with the average correct classification 84.5%, 84.1% and 70.9% respectively.
期刊論文
1.Platt, H. D.、Platt, M. B.(1991)。A note on the use of industry-relative ratios in bankruptcy prediction。Journal of Banking and Finance，15(6)，1183-1194。  new window
2.Xu, B.、Zhou, Y.、Lu, H.(2005)。An improved accuracy measure for rough sets。Journal of Computer and System Sciences，71，163-173。  new window
3.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)，3507-3516。  new window
4.Pawlak, Z.、Skowron, A.(2007)。Rudiments of rough sets。Information Sciences，177(1)，3-27。  new window
5.Deakin, E. B.(1972)。A discriminant analysis of prediction of business failure。J. Account，3(spring)，167-169。  new window
6.Etemadi, H.、Rostamy, A.、Dehkordi, H.(36(2))。A genetic programming model for bankruptcy prediction: empirical evidence from Iran。Expert Syst，36(2)，3199-3207。  new window
7.Homik, K.、Stinchcombe, M.、White, H.(1990)。Universal approximation of an unknown mapping and its derivatives using multilayer feedforward network。Neural Networks，3，359-366。  new window
8.Li, H.、Sun, J.(2008)。Ranking-order case-based reasoning for financial distress prediction。Knowl-Based Syst，21(8)，868-878。  new window
9.Ravi, V.、Pramodh, C.(2008)。Threshold accepting trained principal component neural network and feature subset selection: application to bankruptcy prediction in banks。Applied Soft Computing，8(4)，1539-1548。  new window
10.Verikas, A.、Kalsyte, Z.、Bacauskiene, M.、Gelzinis, A.(2010)。Hybrid and ensemblebased soft computing techniques in bankruptcy prediction: a survey, Soft。Comput，14(9)，995-1010。  new window
11.Grzymala-Busse, J. W.(1997)。A new version of the rule induction system LERS。Fundamenta Informaticae，31(1)，27-39。  new window
12.Sharda, R.、Wilson, R. L.(1994)。Bankruptcy Prediction Using Neural Networks。Decision Support Systems，11(5)，545-557。  new window
13.Min, S. H.、Lee, J.、Han, I.(2006)。Hybrid Genetic Algorithms and Support Vector Machines for Bankruptcy Prediction。Expert Systems with Applications，31(3)，652-660。  new window
14.Beaver, W. H.(1966)。Financial Ratios as Predictors of Failure。Journal of Accounting Research，4(3)，71-111。  new window
15.Altman, Edward I.(1968)。Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy。The Journal of Finance，23(4)，589-609。  new window
16.Kumar, P. Ravi、Ravi, V.(2007)。Bankruptcy prediction in banks and firms via statistical and intelligent techniques--A review。European Journal of Operational Research，180(1)，1-28。  new window
17.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。  new window
圖書
1.Liu, Huo-sheng(2012)。Financial Accounting Practices for Small and Medium Enterprises。Taipei City：Taiwan Academy of Banking and Finance。  new window
2.Haykin, S.(2008)。Neural Networks and Learning Machines。Pearson Prentice Hall。  new window
3.Vapnik, Vladimir N.(1998)。Statistical Learning Theory。John Wiley and Sons, Inc.。  new window
4.Cristianini, N.、Shawe-Taylor, John(2000)。An Introduction to Support Vector Machines and Other Kernel-based Learning Methods。Cambridge University Press。  new window
圖書論文
1.Grzymala-Busse, J. W.、Slowinski, R.。LERS - a system for learning from examples based on rough sets。Intelligent Decision Support: Handbook of Applications and Advances in Rough Set Theory。London：Kluwer Academic Publishers。  new window
 
 
 
 
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