Financial risk early-warning based on RS-SVM hybrid model

Dongxiao Niu; Shengming Hou; Yunyun Zhang; Xiaoya Sun

doi:10.1117/12.836685

10 July 2009 Financial risk early-warning based on RS-SVM hybrid model

Dongxiao Niu, Shengming Hou, Yunyun Zhang, Xiaoya Sun

Proceedings Volume 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering; 74902I (2009) https://doi.org/10.1117/12.836685
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China

Abstract

This paper put forward and experienced an effective early-warning models based on Rough set (RS) and Support vectors machine (SVM) algorithm. The model make Rough set to reduce the indexes in the financial risk early-warning indexes system, thus reducing the dimensions of the input space of SVM, when treating the reduced data as the input space of SVM, the convergence speed and the classify accuracy can be enhanced obviously. Financial data of listed companies is used to train and test the arithmetic, and the results show that RS-SVM model has good capacity for financial conditions of listed companies in China.

Citation Download Citation

Dongxiao Niu, Shengming Hou, Yunyun Zhang, and Xiaoya Sun "Financial risk early-warning based on RS-SVM hybrid model", Proc. SPIE 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering, 74902I (10 July 2009); https://doi.org/10.1117/12.836685

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