制造业企业财务困境的预测:来自印尼的证据

Iskandar Bukhori, Rita Kusumawati, M. Meilani
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引用次数: 2

摘要

研究目的:本研究旨在检验流动性比率、活动比率、杠杆比率和销售增长对破产前财务困境的预测作用。设计/方法/方法:本研究的样本包括2016年至2019年在印度尼西亚证券交易所上市的制造业公司。样本采用有目的的抽样方法进行选择,共获得334家样本公司。然后,数据分析采用逻辑回归。研究结果:结果显示,本研究中调查的所有财务比率都显著影响财务困境。此外,虽然流动性比率、活动比率和销售增长具有显著的负面影响,但杠杆率对财务困境具有显著的正向影响。实践和理论贡献/原创性:目前,大多数关于破产的研究都集中在破产公司,这是财务困境阶段的最后阶段。同时,本研究试图通过研究破产阶段前的财务困境预测来解决这一差距。从业者/政策影响:这项研究的结果有望帮助利益相关者尽早采取纠正措施,防止财务困境进入破产阶段。研究局限性:该研究仅使用了两年的负利润期,处于严重的流动性阶段,并使用了一个行业部门四年的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Financial Distress in Manufacturing Companies: Evidence from Indonesia
Research aims: This study aims to examine the effect of liquidity ratios, activity ratios, leverage ratios, and sales growth as predictors of financial distress before the bankruptcy stage.Design/Methodology/Approach: The samples of this study included manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the period 2016 to 2019. The samples were selected using the purposive sampling method, and 334 sample companies were obtained. Then, the data analysis employed logistic regression.Research findings: The results revealed that all financial ratios investigated in this study significantly affected financial distress. In addition, while the liquidity ratio, activity ratio, and sales growth had a significant negative effect, the leverage ratio had a significant positive impact on financial distress.Practical and Theoretical contribution/Originality: Currently, most research on bankruptcy has concentrated on bankrupt companies, which are the final phase of the financial distress stage. Meanwhile, the current study attempts to address the gap by researching financial distress prediction before the bankruptcy stage.Practitioner/Policy implications: The results of this study are expected to help stakeholders to take corrective action early to prevent financial distress to the bankruptcy stage.Research limitation: The study only used a negative profit period of two years, at the stage of severe liquidity, and utilized four years of data in one industry sector.
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