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Company financial path analysis using fuzzy c-means and its application in financial failure prediction

    Jiaming LIU Affiliation
    ; Chong WU Affiliation

Abstract

This study investigates the dynamic process of the company’s financial status over years and proposes to design financial path using fuzzy c-means (FCM) approach. FCM is firstly used to quantize company financial paths of behavior over consecutive years. Financial paths are deployed to depict different patterns of failure and to understand the dynamics of financial failure. Then financial failure prediction model is built based on the proposed financial path (FP) approach. Empirical experiment is carried out with data samples of Chinese listed companies. Through analyzing financial path, it is found that there are mainly four patterns of process to terminal failure. Besides, in order to validate the prediction performance of the financial path prediction model, four prevalent financial failure prediction models, logistic regression (LR), support vector machine (SVM), decision tree (DT), and neural network (NN), are deployed to compare with the proposed model respectively. Experimental results show that FP has significantly better financial failure performance than other four models in terms of accuracy, type I error, and type II error. Therefore, the financial path monitors the change of financial status and acts as a prediction tool for financial failure prediction, which is a significant determinant of the financial success. Managers can recognize the financial failure signal in advance and understand their evolution trend on the future. In addition, the financial path prediction model is also an effective supplement to the research field of financial failure analysis and prediction.

Keyword : financial failure, financial dynamics, financial path analysis, fuzzy c-means, forecasting, prediction model

How to Cite
LIU, J., & WU, C. (2018). Company financial path analysis using fuzzy c-means and its application in financial failure prediction. Journal of Business Economics and Management, 19(1), 213-234. https://doi.org/10.3846/16111699.2017.1415959
Published
May 10, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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