ASSESSMENT OF THE FINANCIAL STABILITY OF ENTERPRISES USING NEURAL NETWORKS

Oleksandr Kvashuk, Ina Vapriote, Artem Onyskiv
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Abstract

The enterprise is the primary link of the economic system, the stability of which is an important condition for the effective development of the national economy. The sustainability of the enterprise covers a set of factors that allow it to achieve a controlled state of equilibrium and the ability for sustainable economic growth through effective management of activities. The main component of the company's stability is its financial condition, which ensures marketing and personnel stability, promotes the development of production and technical-technological stability, maintains investment stability, and increases the efficiency of the management process. The study describes a neural network-based approach to assessing the financial condition of enterprises, which enables the assessment of the enterprise's financial condition based on its annual financial reports with high accuracy (over 90 percent). The study examined different neural network approaches to analyzing financial data, including the use of different neural network types, training methods, and input parameter selection. The article also examines the influence of various financial indicators on the financial state of the enterprise and suggests using the most significant financial indicators as input parameters for neural networks.
利用神经网络评估企业的财务稳定性
企业是经济体系的首要环节,其稳定性是国民经济有效发展的重要条件。企业的可持续性包括一系列因素,这些因素使企业能够通过有效的活动管理实现可控的平衡状态和可持续的经济增长能力。企业稳定性的主要组成部分是财务状况,财务状况保证了营销和人员的稳定性,促进了生产发展和技术工艺的稳定性,保持了投资的稳定性,提高了管理过程的效率。本研究介绍了一种基于神经网络的企业财务状况评估方法,该方法可根据企业年度财务报告对企业财务状况进行评估,准确率高达 90% 以上。研究探讨了分析财务数据的不同神经网络方法,包括使用不同的神经网络类型、训练方法和输入参数选择。文章还研究了各种财务指标对企业财务状况的影响,并建议使用最重要的财务指标作为神经网络的输入参数。
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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