{"title":"CHOOSING RATIO IN THE FINANCIAL DISTRESS PREDICTION MODEL","authors":"Nikke Yusnita Mahardini, Bandi","doi":"10.59670/jns.v34i.1227","DOIUrl":null,"url":null,"abstract":"Purpose – This study aims to find a financial distress prediction model that is suitable for Indonesian companies.Design/methodology/approach – The sample in this study amounted to 150 data. The research sample was grouped into financial distress and non-financial distress. Research data is sourced from the Indonesia Stock Exchange. Discriminant analysis is used to test data and generate financial distress prediction models for manufacturing companies in Indonesia.Findings – Results show that the financial ratios that contribute to the financial distress prediction model are the ratios of profitability, liquidity, and efficiency.Practical Implications – The resulting model can contribute and as a basis for the development of future studies on relevant, robust, and accurate corporate financial distress early warning systems that will help stakeholders to respond to potential bankruptcies accordingly and on timeOriginality – Research on new models to predict corporate bankruptcy in Indonesia which is a developing country is still rare. Most of the literature still uses the Altman Z-Score model.","PeriodicalId":37633,"journal":{"name":"Journal of Namibian Studies","volume":"319 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Namibian Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59670/jns.v34i.1227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Arts and Humanities","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose – This study aims to find a financial distress prediction model that is suitable for Indonesian companies.Design/methodology/approach – The sample in this study amounted to 150 data. The research sample was grouped into financial distress and non-financial distress. Research data is sourced from the Indonesia Stock Exchange. Discriminant analysis is used to test data and generate financial distress prediction models for manufacturing companies in Indonesia.Findings – Results show that the financial ratios that contribute to the financial distress prediction model are the ratios of profitability, liquidity, and efficiency.Practical Implications – The resulting model can contribute and as a basis for the development of future studies on relevant, robust, and accurate corporate financial distress early warning systems that will help stakeholders to respond to potential bankruptcies accordingly and on timeOriginality – Research on new models to predict corporate bankruptcy in Indonesia which is a developing country is still rare. Most of the literature still uses the Altman Z-Score model.