基于神经网络的财务数据处理与预测模型分析

Wenjie Xiong, U. Comite
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引用次数: 0

摘要

财务数据是企业生存的关键。通过对这些财务数据的分析,不仅可以看到公司自身的竞争优势,还可以看到公司在同行业中的地位,这可以使公司更容易制定财务计划。改善财务状况,提高经济效益,实现企业的可持续发展。本文从偿债能力、经营能力、盈利能力和发展能力四个方面构建了企业财务风险的评价指标体系,并通过神经网络模型建立了熵权TOPSIS法和SOM神经网络财务风险评价模型。解决了现有层次分析法和模糊评价理论权重难以确定、导致评价结果准确性不高的缺点。同时,在系统的理论研究和实证分析的基础上,计算出财务风险评价得分,使财务风险的评价和计算更加科学有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Financial data processing and forecasting model analysis based on neural network
Financial data is the key to the survival of an enterprise. The analysis of these financial data can not only see the company's own competitive advantages, but also can see the position of the company in the same industry, which can make it easier for the company to formulate financial plans. Improve financial status, improve economic efficiency, and achieve sustainable development of enterprises. This paper constructs an evaluation index system of a company's financial risk from four aspects: solvency, operating ability, profitability and development ability, and establishes the entropy weight TOPSIS method and the SOM neural network financial risk evaluation model through the neural network model. It solves the disadvantage that the weight of the existing AHP and fuzzy evaluation theory is difficult to determine, which leads to the low accuracy of the evaluation result. At the same time, the financial risk evaluation score is calculated on the basis of systematic theoretical research and empirical analysis, which makes the evaluation and calculation of financial risk more scientific and effective.
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