50,344 research outputs found
Hybrid model using logit and nonparametric methods for predicting micro-entity failure
Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper
by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to
detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods
(Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as
either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and
Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method
implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic
variables complement financial ratios for bankruptcy prediction
Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress
Recently, applying the novel data mining techniques for evaluating enterprise
financial distress has received much research alternation. Support Vector
Machine (SVM) and back propagation neural (BPN) network has been applied
successfully in many areas with excellent generalization results, such as rule
extraction, classification and evaluation. In this paper, a model based on SVM
with Gaussian RBF kernel is proposed here for enterprise financial distress
evaluation. BPN network is considered one of the simplest and are most general
methods used for supervised training of multilayered neural network. The
comparative results show that through the difference between the performance
measures is marginal; SVM gives higher precision and lower error rates.Comment: 13 pages, 1 figur
European exchange trading funds trading with locally weighted support vector regression
In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series
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