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Financial early warning dynamic evaluation model based on support vector machine

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Parent Category: Contents No.4 2016

Category: Economy and management

Created on 02 October 2016

Last Updated on 02 October 2016

Published on 02 October 2016

Written by Super User

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Authors:

Qi Zhu, College of Finance and Accounting, Anhui Sanlian University, Hefei Anhui, China

Abstract:

Purpose. The issue of bankruptcy prediction and taking corresponding effect-reduction measures has become an important research topic in the field of economy. The article mainly studies the issue how to improve the support vector machine (SVM) based on prediction effect of the financial crisis early-warning model.

Methodology. In view of the improved method of the kernel function exported Riemannian geometry structure, the financial crisis early-warning system based on the support vector machine (SVM) algorithm has been developed.

Findings. The improved SVM model can effectively reduce the number of support vectors so that the model can feature better capacity for generalization, and provide a more accurate classification for unknown samples. The improved SVM model has increased the classification accuracy for the original training and testing sample set.

Originality. An actual analysis on an improved algorithm for kernel function has been conducted based on the data information, according to the zoom level of data information adjustment feature mapping, giving the expression of the algorithm of Riemann measures on the polynomial kernel function. There has been no other literature describing the related study yet.

Practical value. For the financial crisis early warning issue, provided its scale is not large, and in consideration of the enterprise’s actual interests, it is often worthwhile to pursue a better prediction effect. In this case, the application of the SVM model with an improved kernel function is a good solution.

References/Список літератури

1. Koyuncugil, A.S. and Ozgulbas, N., 2012. Financial early warning system model and data mining application for risk detection. Expert Systems with Applications, Vol. 39, No. 6, pp. 6238–6253.

2. Li, J., Qin, Y., Yi, D., Li, Y. and Shen, Y., 2014. Feature Selection for Support Vector Machine in the Study of Financial Early Warning System. Quality and Reliability Engineering International, Vol. 30, No. 6, pp. 867–877.

3. Candelon, B., Dumitrescu, E.I. and Hurlin, C., 2012. How to evaluate an early-warning system: Toward a unified statistical framework for assessing financial crises forecasting methods. IMF Economic Review, Vol. 60, No. 1, pp. 75–113.

4. DeYoung, R. and Torna, G., 2013. Nontraditional banking activities and bank failures during the financial crisis. Journal of Financial Intermediation, Vol. 22, No. 3, pp. 397–421.

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7. Chen, M.Y., 2011. Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, Vol. 38, No. 9, pp. 11261–11272.

8. Youn, H. and Gu, Z., 2010. Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model. International Journal of Hospitality Management, Vol. 29, No. 1, pp. 120–127.

9. Chen, M.Y., 2011. Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, Vol. 38, No. 9, pp. 11261–11272.

10. Aydin, A.D. and Cavdar, S.C., 2015. Prediction of Financial Crisis with Artificial Neural Network: An Empirical Analysis on Turkey. International Journal of Financial Research, Vol. 6, No. 4, pр. 36–46.

11. Kerzman, N., 1971. The Bergman kernel function. Differentiability at the boundary. Mathematische Annalen, Vol. 195, No. 3, pp. 149–158.

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