The Journal of Quantitative & Technical Economics 2019(10), pp. 149-165.
ISSN/ISBN: Not available at this time. DOI: 10.13653/j.cnki.jqte.2019.10.009
Note - this is a foreign language paper: CHI
Abstract: Research Objectives:Benford’s law is a common method for financial data quality detection.By introducing Benford’s law into the financial early warning Logistic model,the effective financial data quality variables are added to improve the prediction accuracy of the early warning model.Research Methods:Benford’s law is used to test the quality of financial data,and the Benford factors are constructed.Combining it with financial variables to establish a financial early warning Logistic model.Taking the 2000～2017 Chinese A-share listed companies as a sample,and using Lasso to select the explanatory variables and to determine the optimal model.Verifying that the addition of Benford factors can effectively improve the prediction accuracy of Logistic model.Research Findings:The Benford factor can reflect the quality of enterprise financial data and has some correlation with the ST company.The Benford factor can be used to improve the prediction accuracy of the financial early warning Logistic model.Research Innovations:To introduce the Benford’s law into the financial early warning Logistic model,to propose a construction method of the Benford factor,and to establish the financial early warning Benford-Logistic model.Research Value:To improve prediction accuracy of Logistic model,and to provide an effective modeling method for enterprise financial early warning.The research results can be used as risk assessment and default prediction.
Bibtex:
@article{,
author = {Yang, Guijun and Zhou, Yameng; and Sun, Lingli},
title = {Enterprise Financial Early Warning Method Based on Benford-Logistic Model},
year = {2019},
journal = {The Journal of Quantitative & Technical Economics},
number = {10},
pages = {149--165},
doi = {10.13653/j.cnki.jqte.2019.10.009},
}
Reference Type: Journal Article
Subject Area(s): Accounting, Economics