Financial Risk Prediction for Listed Companies using IPSO-BP Neural Network

doi:10.23940/ijpe.19.04.p16.12091219

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (4): 1209-1219.doi: 10.23940/ijpe.19.04.p16.12091219

Previous Articles Next Articles

Financial Risk Prediction for Listed Companies using IPSO-BP Neural Network

Sha Li^{a, *} and Yu Quan^b

a Zhejiang Industry Polytechnic College, Shaoxing, 312000, China;
b Science Technology Bureau of Shaoxing, Shaoxing, 312000, China

Revised on ; Accepted on
Contact: E-mail address: 279268221@qq.com
About author:Sha Li is a lecturer at Zhejiang Industry Polytechnic College. She received her master's degree from Jiangxi Normal University. Her research interests include business accounting and financial modeling. She was a visiting scholar at Zhejiang Normal University in 2018. Yu Quan is an engineer in the Science Technology Bureau of Shaoxing. His research interests include cloud computing and algorithm research.

Abstract

Abstract: Manufacturing is an important part of the market economy. Judgment and analysis of financial risks in the manufacturing industry help promote the healthy development of the real economy. A sample of manufacturing companies for the period 2015-2017 is selected. First, the financial indicators of the companies are screened using principal component analysis. Second, Back Propagation (BP) neural network parameters are optimized using improved particle swarm optimization (IPSO), and a financial risk early warning model based on IPSO-BP is constructed. Finally, an empirical analysis is performed. The analysis results reveal that the model can accurately predict the financial risks of manufacturing companies and provide valuable guidance in the form of a company financial risk warning.

Key words: financial risk, principal component analysis, improved particle swarm optimization BP

Sha Li and Yu Quan. Financial Risk Prediction for Listed Companies using IPSO-BP Neural Network [J]. Int J Performability Eng, 2019, 15(4): 1209-1219.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1 P. Fitzpatrick,“A Comparison of Ratios of Successful Industrial Enterprises with those of Failed Firms,” Certified Public Accountant, Washington D. C., 1932
2 W. H. Beaver, “Financial Ratios as Predictors of Failure,” Journal of Accounting Research, Vol. 4, No. 1, pp. 71-111, 1996
3 E. I. Altman, “Financial Ration, Discriminant Analysis and the Prediction of Coproate Bankruptcy,” The Journal of Finance, Vol. 23, No. 4, pp. 589-609, 1968
4 S. N. Wu, “A Study of Models for Predicting Financial Distress in China's Listed companies,”Economic Research Journal, No. 6, pp. 46-55, 2001
5 S. E.Yang and L. Huang, “Financial Crisis Warning Model based on BP Neural Network,”Systems Engineering-Theory & Practice, No. 1, pp. 12-18, 2005
6 D. D. Zhao, J. C. Ding,S. C. Chai, “Systemic Financial Risk Prediction using Least Squares Support Vector Machines,” Modern Physics Letters B, Vol. 32, No. 17, pp. 1850183, 2018
7 A. E. Yamami, S. Ahriz, K. Mansouri, M. Qbadou,E. H. Illoussamen, “Rethinking IT Project Financial Risk Prediction using Reference Class Forecasting Technique,” in Proceedings of 2018 4th International Conference on Optimization and Applications (ICOA), IEEE, 2018
8 K. Valaskova and T. Kliestik, “Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities using Regression Analysis,”Sustainability, pp. 2144, 2018
9 F. Sun, “The Role of Cash Flow in Financial Early Warning of Agricultural Enterprises based on Logistic Model,” in Proceedings of IOP Conference Series: Earth and Environmental Science, Vol. 108, No. 4, 2018
10 Y. Zhang, “Financial System Crisis Early Warning Model Analysis based on Neural Network with Entropy Method,” inProceedings of 2018 International Conference on Robots & Intelligent System (ICRIS), pp. 560-563, IEEE, 2018
11 M. Apan, A. Öztel,M. İslamoğlu, “Comparative Empirical Analysis of Financial Failures of Enterprises with Altman Z-Score and VIKOR Methods: BIST Food Sector Application,” Australasian Accounting, Business and Finance Journal, Vol. 12, No. 1, pp. 77-101, 2018
12 C. Wu, “Use of Hybrid Fuzzy C-Means and Probabilistic Neural Network based on Improved Particle Swarm Optimization in the Prediction of Financial Distress,” Operations Research and Management Science, Vol. 27, No. 2, pp. 106-114, 2018
13 B. Song, “Analysis of Big Data Financial Early Warning Index based on System Structure Model,”Friends of Accounting, No. 9, pp. 95-99, 2017
14 Y. P. Wang, “A Research of Financial Early Warning about Logistic Model based on FOA,” Journal of Systems Science and Mathematical Sciences, Vol. 37, No. 2, pp. 573-586, 2017
15 X. Y.Song and N. S. Chen, “Financial Prediction System based on Combination of Genetic Algorithm with BP Neural Network,” Journal of Shanghai Jiaotong University, Vol. 50, No. 2, pp. 313-316, 2016

[1]	Zhongbo Wang and Zhilin Suo. Data Analysis of Hybrid Principal Component for Rural Land Circulation Management based on Gray Relation Algorithmic Models [J]. Int J Performability Eng, 2019, 15(7): 1976-1987.
[2]	Xin Xu, Meng Cao, Jiawei Ding, Hong Gu, and Wenjuan Lu. Emotional Recognition of EEG Signals based on Fractal Dimension [J]. Int J Performability Eng, 2019, 15(11): 3072-3080.
[3]	Yanhua Cao, Jinmao Guo, Yong Li, and Huiqiang Lv. Engine Life Prediction based on Degradation Data [J]. Int J Performability Eng, 2018, 14(12): 2905-2914.

Financial Risk Prediction for Listed Companies using IPSO-BP Neural Network

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Recommended 0