Altman的Z-score和T. Jury基于现金的信用风险模型与生产公司的应用以及2016-2022年的数据进行了比较分析

Alexey Litvinenko
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引用次数: 0

摘要

研究问题:在本文中,作者回答了以下研究问题:1)基于现金流原理的信用风险模型的潜在优势是什么?2)权责发生制信用风险模型的弱点是什么?3)在分析公司时,结合使用现金和权责发生制信用风险建模方法有什么好处?动机:目前还没有基于权责发生制的信用风险模型与基于现金的信用风险模型的比较研究,并没有将其应用于一家在证券交易所上市的生产公司。然而,对于投资者、审计人员和金融机构来说,重要的是要知道这两种模型在解释分析结果、确定公司破产前阶段和信用风险违约方面是否存在差异。思路:本文重点对基于现金的信用风险模型和基于权责发生制的信用风险模型进行了比较分析。作者将其应用于制造企业的案例,比较了基于现金的信用风险模型和基于权责发生制的信用风险模型确定违约概率的有效性。数据:分析的数据来自利纳斯农业集团2016-2022年年度报告、管理报告和审计报告。公司信息来自纳斯达克波罗的海,在那里利纳斯农业集团有其股票交易。工具:采用定量计算与定性分析相结合的混合研究方法。本文在改进的Timothy Jury模板的基础上,详细阐述了基于现金的信用风险模型。用于比较和分析的基于权责发生制的模型是Altman的Z-Score模型。研究发现:研究结果表明,现金基础模型在确定信用风险和违约概率方面更为有效。以现金为基础的模型显示,该制造公司在7年中有4年处于高风险违约,而Altman的Z-Score显示,该公司在7年中有5年处于中度风险灰色地带,最后两年模型显示该公司处于绿色地带。笔者建议金融机构、财务管理者和投资者使用现金为基础的信用风险模型或将其与权责发生制模型相结合。贡献:本文对现金制和权责发生制信用风险模型的比较有所贡献,并强调了它们的优缺点。它帮助投资者、审计人员、企业主和金融专业人士决定使用哪种信用风险模型进行分析,以确定公司破产前的状态,避免不良贷款,提高投资决策。鼓励学术界对权责发生制信用风险模型和现金信用风险模型进行进一步的研究和比较,努力建立能够尽可能精确地分析数据的最终信用风险模型。
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A comparative analysis of Altman's Z-score and T. Jury's cash-based credit risk models with the application to the production company and the data for the years 2016-2022
Research Questions: In the present paper the author answers the following research questions: 1) What are the potential strengths of the credit risk model based on the cash flow principle? 2) What are the weaknesses of the accrual-based credit risk model? 3) What are the benefits of the combined use of both cash-based and accrual-based credit risk modeling methods when analyzing companies? Motivation: nowadays there are no researches comparing the accrual-based credit risk model to a cash-based credit risk model with the application to a production company trading its shares on Stock Exchange. However, for investors, auditors and financial institutions it is important to know if there is a difference between these two models in the interpretation of analysis results, and determination of prebankruptcy stage of the company and credit risk default. Idea: in this paper, the author has focused on the comparative analysis of the cash-based credit risk model and the accrual-based credit risk model. The author applies it to the case of a manufacturing company and compares the effectiveness of determining the probability of default using a cash-based credit risk model and an accrual-based credit risk model. Data: the data analysed is obtained from the annual reports, managerial reports and auditor’s reports of Linas Agro Group for the years 2016-2022. The company information is taken from Nasdaq Baltic where Linas Agro Group has its shares traded. Tools: mixed research methods were used, combining quantitative calculations with analysis based on qualitative information. The author elaborates on the cash-based credit risk model based on the improved Timothy Jury’s template. The accrual-based model chosen for comparison and analysis is Altman’s Z-Score model. Findings: The results of the study have shown that the cash-based model is more effective in determining credit risk and default probability. The cash-based model indicated a high-risk default for the manufacturing company in four years out of seven years, while Altman’s Z-Score showed the company to be in the moderate risk grey zone in five years out of seven, and the two last years the model indicated the company in the green zone. The author suggests to financial institutions, financial managers, and investors using a cash-based credit risk model or combination of it with the accrual-based model. Contribution: the paper contributes to the knowledge about the comparison of cash-based and accrual-based credit risk models and emphasizes their strengths and weaknesses. It helps investors, auditors, business owners, and finance professionals to make a decision about which credit risk model to use for the analysis to determine the pre-bankruptcy state of the company, avoid bad loans and improve investment decision-making. It also encourages the academic society for further research and comparison on the topics of accrual-based and cash-based credit risk models in the strive to develop the ultimate credit risk model capable to analyse the data as precisely as possible.
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