An Application of Support Vector Machine to Companies’ Financial Distress Prediction
作者： Xiao-Feng Hui , Jie Sun
DOI: 10.1007/11681960_27
关键词: Support vector machine 、 Backpropagation 、 Machine learning 、 Cross-validation 、 Linear discriminant analysis 、 Artificial neural network 、 Artificial intelligence 、 Logistic regression 、 Regression analysis 、 Computer science 、 Stability (learning theory) 、 Data mining
摘要: … Because of the importance of companies’ financial distress prediction, this paper applies support vector machine (SVM) to the early-warning of financial distress. Taking listed …
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Desmond Fletcher, Ernie Goss, Forecasting with neural networks Information & Management. ,vol. 24, pp. 159- 167 ,(1993) , 10.1016/0378-7206(93)90064-Z
Nello Cristianini, John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods ,(2000)
Francis E.H Tay, Lijuan Cao, Application of support vector machines in financial time series forecasting Omega. ,vol. 29, pp. 309- 317 ,(2001) , 10.1016/S0305-0483(01)00026-3
Russell L. Ackoff, Optimization + objectivity = optout European Journal of Operational Research. ,vol. 1, pp. 1- 7 ,(1977) , 10.1016/S0377-2217(77)81003-5
Kyoung-jae Kim, Financial time series forecasting using support vector machines Neurocomputing. ,vol. 55, pp. 307- 319 ,(2003) , 10.1016/S0925-2312(03)00372-2
William H. Beaver, Financial Ratios As Predictors of Failure Journal of Accounting Research. ,vol. 4, pp. 71- 111 ,(1966) , 10.2307/2490171
Parag C. Pendharkar, A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem Computers & Operations Research. ,vol. 32, pp. 2561- 2582 ,(2005) , 10.1016/J.COR.2004.06.023
Carlos Serrano-Cinca, Self organizing neural networks for financial diagnosis decision support systems. ,vol. 17, pp. 227- 238 ,(1996) , 10.1016/0167-9236(95)00033-X
James A Ohlson, None, Financial Ratios and the Probabilistic Prediction of Bankruptcy Journal of Accounting Research. ,vol. 18, pp. 109- 131 ,(1980) , 10.2307/2490395
Tony Van Gestel, Bart Baesens, Johan A.K. Suykens, Dirk Van den Poel, Dirk-Emma Baestaens, Marleen Willekens, Bayesian kernel based classification for financial distress detection European Journal of Operational Research. ,vol. 172, pp. 979- 1003 ,(2006) , 10.1016/J.EJOR.2004.11.009