A K-means++-improved Radial Basis Function Neural Network Model for Corporate Financial Crisis Early Warning: An Empirical Model Validation for Chinese Listed Companies

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Risk Model Validation Pub Date : 2019-03-18 DOI:10.21314/jrmv.2020.223

Danyang Lv, Chong Wu, Linxiao Dong

引用次数: 0

Abstract

An early warning of corporate financial crises has long been the focus of investors and enterprises. Integrated early warning models for financial crises perform better than normal models, but most integrated models are very complex, elusive and computationally time-consuming. This paper aims to simplify the early warning model for financial crises by collecting and analyzing the financial data of Chinese special treatment (ST) companies, normally listed companies and cancel special treatment (CST) companies. To further predict the financial risks of companies, we put forward a finance-predicting model based on the k-means++ algorithm and an improved radial basis function neural network (RBF NN), and we compare their respective statistics. We indicate by experiment that combining k-means++ with the improved RBF NN helps to better predict financial risks for companies, which is effective in the risk control of financial management.

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企业财务危机预警的K-means++改进径向基函数神经网络模型——中国上市公司的实证模型验证

企业金融危机的预警一直是投资者和企业关注的焦点。金融危机的综合预警模型比普通模型表现更好，但大多数综合模型非常复杂、难以捉摸，计算耗时。本文旨在通过收集和分析中国特殊待遇公司、正常上市公司和取消特殊待遇公司的财务数据，简化金融危机预警模型。为了进一步预测公司的财务风险，我们提出了一个基于k-means++算法和改进的径向基函数神经网络（RBF NN）的财务预测模型，并对它们各自的统计数据进行了比较。实验表明，将k-means++与改进的RBF神经网络相结合，有助于更好地预测企业的财务风险，在财务管理的风险控制中是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Risk Model Validation BUSINESS, FINANCE-

CiteScore

1.20

自引率

28.60%

发文量

期刊介绍： As monetary institutions rely greatly on economic and financial models for a wide array of applications, model validation has become progressively inventive within the field of risk. The Journal of Risk Model Validation focuses on the implementation and validation of risk models, and aims to provide a greater understanding of key issues including the empirical evaluation of existing models, pitfalls in model validation and the development of new methods. We also publish papers on back-testing. Our main field of application is in credit risk modelling but we are happy to consider any issues of risk model validation for any financial asset class. The Journal of Risk Model Validation considers submissions in the form of research papers on topics including, but not limited to: Empirical model evaluation studies Backtesting studies Stress-testing studies New methods of model validation/backtesting/stress-testing Best practices in model development, deployment, production and maintenance Pitfalls in model validation techniques (all types of risk, forecasting, pricing and rating)