预测公司破产的财务困境分析--印度一家公共部门公司的案例研究

A. V. Rejimon, M. Usha, Dr. William Castillo-González
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引用次数: 0

摘要

目的:本研究探讨了企业的流动性及其对财务困境的影响,财务困境可能会也可能不会增加企业破产的几率。本研究还分析了公司的盈利能力、现金状况和偿债能力:我们利用一家政府制造业上市公司的数据,测算其在 2015 年至 2019 年期间的财务困境和破产概率,以检验其陷入财务困境的几率。研究中用于评估的财务模型有Altman z-score模型、Logit概率模型和Falmur模型:研究发现,该公司在最初几年有可能破产,但后来挺过了破产期。研究还发现,公司的流动性和偿付能力不达标:研究结果拓展了我们的理论认识,也为降低公司资不抵债、破产和财务困境的几率,以及保持公司适当的财务健康状况提供了有价值的指导:虽然许多实证研究调查了流动性状况之间的关系及其对整个行业中陷入财务困境的公司的影响,但大多数研究并未考虑单个公司或企业陷入财务困境的影响。大多数已发表的研究使用统计工具来评估财务困境。本研究采用多重判别财务模型分析法。多重判别财务模型分析在决定财务困境问题的补救措施方面非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Financial distress analysis for the prediction of corporate bankruptcy – a case study of a public sector company in India
Purpose: The present study examines the liquidity of the firm and its impact on financial distress, which may or may not increase the chances of bankruptcy. The study also analyzes the profitability, cash position, and solvency of the firm.Design/methodology/approach: We use the data of a listed Government manufacturing company and measure the financial distress and probabilities of bankruptcy to test the chances of financial distress during the period between 2015 and 2019. The financial models used for evaluation in the study are the Altman z-score model, Logit Probability model, and Falmur model.Findings: The study found that there was a chance of bankruptcy in the initial years, but later, it survived the bankruptcy. The study also established that the liquidity and solvency of the firm were not up to the standard.Practical implications: The result of the study extends our theoretical understanding and also provides valuable guidelines to reduce the chance of insolvency, bankruptcy, and financial distress of firms and to maintain the proper financial health of the firm.Originality/value: While many empirical studies investigate the relationship between liquidity position and its impact on financially distressed firms in the industry as a whole, but most do not consider the impact of financial distress in an individual firm or company. Most of the published studies use statistical tools for the evaluation of financial distress. This study uses Multiple Discriminant financial model analysis. Multiple Discriminant financial model Analyses are very useful in deciding remedial actions for financial distress problems.
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