The Analysis of Altman Model and Ohlson Model in Predicting Financial Distress of Manufacturing Companies in the Indonesia Stock Exchange

Authors

  • Elsa Imelda Tarumanagara University, Jakarta, Indonesia
  • Ignacia Alodia Tarumanagara University, Jakarta, Indonesia

DOI:

https://doi.org/10.52962/ipjaf.2017.1.1.4

Keywords:

Altman Model, Ohlson Model, Multiple Discriminant Analysis, Logit Analysis, Bankruptcy

Abstract

The purpose of this research is to examine the accuracy of the Altman Model and the Ohlson Model in Bankruptcy Prediction.The research population is all companies who are listed on the Indonesian Stock Exchange. The sample of the research is 40 manufacturing companies listed on the Indonesian Stock Exchange in the period of 2010-2014 that are divided into companies with financial distress and those without financial distress.The data analysis technique is the Multiple Discriminant Analysis and Logit Analysis. The Multiple Discriminant Analysis is derived from the Altman Model while the Logit Analysis is derived from theOhlson Model. The results show that the Ohlson Model and the Logit Analysis are more accurate than the Altman Model and the Multiple Discriminant Analysis in predicting bankruptcy of manufacturing firms in the Indonesian Stock Exchange (BEI) in 2010-2014. Also, the results of the study reveal that the ratio of retained earnings to total assets; earning before interest and taxes to total assets; market value of equity to total liabilities; sales to total assets; and debt ratio, return on assets, working capital to total assets and net income were negative in the last two years. Hence constitutes the benchmark for consideration in determining the financial distress of a company.

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References

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Published

2017-01-01

Issue

Section

Main Section

How to Cite

The Analysis of Altman Model and Ohlson Model in Predicting Financial Distress of Manufacturing Companies in the Indonesia Stock Exchange. (2017). Indian-Pacific Journal of Accounting and Finance, 1(1), 51-63. https://doi.org/10.52962/ipjaf.2017.1.1.4

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