利用人工神经网络估计企业财务失败:土耳其制造业模型研究

Lokman Kantar, Ayşegül Ertuğrul Ayrancı
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引用次数: 0

摘要

企业需要在财务上取得成功,才能实现可持续增长和企业价值最大化。企业的财务失败是一种由企业经理、企业股东、向企业提供贷款的金融机构和政府仔细监控的情况。因此,在本研究中,试图对153家在土耳其经营并在Borsa Istanbul交易的制造公司的财务失败进行估计。本研究采用2009-2021年的年度财务报表,优选人工神经网络作为估计方法。Altman的Z分数被用来定义金融失败。在人工神经网络模型中,采用13个财务比率作为输入变量。作为输出变量,低于Altman计算的Z分数1.81的企业被认为是不成功的,不成功的企业被赋值为1,其他企业被赋值为0。这个由0和1值组成的虚拟变量被接受为输出变量。根据研究结果,最初被认为是金融失败的1631个观察中有1427个被正确估计,成功率达到了87.49%。这些发现将为企业和所有利益相关者提前确定财务失败的原因提供重要的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating Financial Failure in Businesses Using Artificial Neural Networks: Turkish Manufacturing Industry Model Study
Businesses need to be financially successful to achieve sustainable growth and maximise firm value. The financial failure of businesses is a situation that is carefully monitored by business managers, shareholders of the business, financial institutions that lend to the business, and the government. For this reason, in this study, the financial failure of 153 manufacturing companies operating in Turkey and traded on Borsa Istanbul has been tried to be estimated. In the research, the annual financial statements between the years 2009-2021 were used and artificial neural networks were preferred as the estimation method. Altman's Z score was used to define financial failure. In the artificial neural network model, 13 financial ratios were used as input variables. As the output variable, the firms that were below the value of 1.81 calculated as the Z score by Altman were considered unsuccessful, and the unsuccessful firms were assigned a value of 1 and the others a value of 0. This dummy variable consisting of 0 and 1 values is accepted as the output variable. According to the findings of the study, 1427 of 1631 observations that were initially considered to be financial failures were correctly estimated and a very high success rate of 87.49% was achieved. The findings will provide an important advantage to businesses and all stakeholders in terms of determining the causes of financial failure in advance.
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