PROCEEDING The 3rd-ICIBSoS

FINANCIAL DISTRESS PREDICTION ON PUBLIC LISTED BANKS IN INDONESIA STOCK EXCHANGE

ICIBSoSDionysia Kowanda

Faculty of Economics, Gunadarma University

Rowland Bismark Fernando Pasaribu

Faculty of Economics, Gunadarma University

Muhammad Firdaus

Faculty of Economics, Gunadarma University

 

 ABSTRACT

A financial distress of company should be able anticipated smartly by its management to rerun the business without having any loss due to business failure. Thus, we need a model which could provide an early signal to company the probability of financial distress so that remedial efforts can be run immediately. This study aims to explore CAMEL’s ratio as an early classificator, and also to reexamine the capacity of CAMEL ratio as a predictor of banks distress. Using a logit binary to classified the probability of distress and non-distress, then multiple regression to determines the ability of financial ratios as a predictor of distress issuers which obtained the following results: a) An exploration CAMEL ratios as an early classificator resulting high classification capacity with a range of 78.7%-91.4%, Furthermore, when CAMEL ratio were used as a predictors, still resulted a high of capability to classify samples accurately by 82.4%.

Keyword : CAMEL, distress, financial distress, logit binary, rasio

 

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FINANCIAL DISTRESS PREDICTION ON PUBLIC LISTED BANKS IN INDONESIA STOCK EXCHANGE

 

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