Analisis Perbandingan Model Prediksi Financial Distress pada Sub Sektor Textile dan Garment

Agil Krisna Rivanda, Ade Imam Muslim


Abstract. The aims of this study is to identify the variables used in financial distress prediction dominantly and to identify the best accuraction and clasification from the financial distress prediction models. The objects are 15 textile and garment companies listed in Indonesia Stock Exchange since 2010 to 2018 using logistic regression and multiple discriminant analysis methods. The variables used financial ratios indicators from the aspects of the operation capacity, liquidity, profitability, solvency, asset management capacity and growth capacity. The results of the study suggest that the model based on a logit function out performs the classification accuracy of the discriminant model. The classification power created by logistic regression was 85.92% while the classification power created by multiple discriminant analysis was 83.70%. The best accuracy came from logistic regression models with 71 from 71 observations predicted fall into health firm category were classified correctly (100%), and 19 from 64 textile and garment companies repondends predicted fall into financial distress category were classified correctly (70,30%). The most significant predictors of impeding firms failure appear to be debt to equity ratio, return on assets ratio, return on equity ratio and working capital to total assets.


Keywords. Financial distress prediction; Financial ratio; Logistic regression, Multivariate discriminant analysis


Financial distress prediction; Financial ratio; Logistic regression, Multivariate discriminant analysis

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