PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE

Fuqiang Wang

29 citations0/0 files storedAsia Pacific Journal of Operational Research

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Financial Distress and Bankruptcy Prediction Artificial Intelligence in Healthcare Rough Sets and Fuzzy Logic

Abstract

Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters 1996A survey of business failures with an emphasis on prediction methods and industrial applications 2002A genetic algorithm application in bankruptcy prediction modeling 2000The integrated methodology of rough set theory and artificial neural network for business failure prediction 2005Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank

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2010PENYEBAB, DAMPAK, DAN PBEDIKSI DARI FINANCIAL DISTRESS SERTA SOLUSI UNTUK MENGATASI FINANCIAL DISTRESS 2018Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”

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