基于计算智能和神经网络方法的财务预警模型优化系统

M. Kathikeyan, A. Roy, S. S. Hameed, P. R. Gedamkar, G. Manikandan, Vinita Kale
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引用次数: 3

摘要

目前,在印度,面临财务困难的企业数量迅速增加,企业对风险的整体抵御能力较低。随着时间的推移,传统的财务会计已经发展成为管理会计。在计算智能时代,会计师需要提高他们的技能和知识,为客户的业务增加更多价值。为了建立企业财务危机预警系统,本文选取五家公司2019年至2021年的两年数据作为训练样本,选取五家公司的数据作为预测样本,目的是发现企业财务危机的预警信号,提前提醒管理者,使他们能够迅速果断地采取行动,消除潜在的威胁。根据测试结果,选择了最能反映能源行业财务困境的6个指标变量作为建模的起点。为了更好地对企业财务危机管理进行预警,减少企业财务危机的发生,本文以上市公司为例,从偿债能力、发展能力、经营能力、现金流能力五个盈利能力方面构建了财务危机预警指标体系。我们使用操作和贝叶斯神经网络模型分析和评估2019年至2021年的数据,以预测2021年的财政风险。在比较两种模型时,神经网络BP模型在拟合数据和预测未来方面优于逻辑模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization System for Financial Early Warning Model Based on the Computational Intelligence and Neural Network Method
In India right now, there is a rapid increase in the number of businesses experiencing financial difficulties, and businesses’ overall resilience to risks is low. As a result of advances and changes throughout time, traditional financial accounting has developed into management accounting. Accountants will need to improve their skills and knowledge to add more value to their clients’ businesses in the age of computational intelligence. To establish a corporate financial crisis early warning system, this paper selects the two-year data of five companies from 2019 to 2021 for training samples and the data of five companies for prediction samples, with the goal of detecting the early warning signs of a corporate financial crisis and alerting managers in advance so that they can take swift, decisive action to eliminate any potential threats. Based on the results of the tests, the 6 index variables that best capture the energy industry’s financial woes have been chosen as the starting point for the modeling. Using In order to better the early-warning effect of enterprise financial crisis management and reduce the occurrence of enterprise financial crises, a financial crisis early-warning indicator system was developed from the five aspects of profitability: debt-paying ability, development ability, operation ability, and cash flow ability, using listed companies as examples.crises. We analyse and evaluate data from 2019 to 2021 using operational and Bayesian neural network models, to foresee fiscal risk in 2021. When comparing the two models, neural network for BP model does better than the logical model in terms of how well it fits the data and how well it predicts the future.
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