Article2012

PREDICTING FINANCIAL DISTRESS OF PUBLIC COMPANIES LISTED IN AMMAN STOCK EXCHANGE

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Financial Distress and Bankruptcy Prediction Financial Analysis and Corporate Governance Corporate Insolvency and Governance

Abstract

This study investigates the role of a set of financial ratios in predicting financial distress of publicly listed companies in Jordan. Using Logistic Regression and Discriminant Analysis a comparison has been made between the two models to determine which is more appropriate to use as well as which of the financial ratios are statistically significant in predicting the financial distress of Jordanian companies. During the period 2007 to 2011, the results show that both logistic regression and discriminant analysis can predict financial distress, and that Return on Equity (ROE) and Return on Assets (ROA) are the most important two financial ratios, which help in predicting the financial distress of public companies listed in Amman stock Exchange.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1977ZETATM analysis A new model to identify bankruptcy risk of corporations 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)1985ASSESSING THE VULNERABILITY TO FAILURE OF AMERICAN INDUSTRIAL FIRMS: A LOGISTIC ANALYSIS 2004Predicting corporate failure: empirical evidence for the UK 1980Early Warning Indicators of Business Failure 2002CORPORATE DISTRESS PREDICTION MODELS IN A TURBULENT ECONOMIC AND BASEL II ENVIRONMENT

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2014Cash Holdings and Corporate Profitability: Some Evidences form Jordan 2017MEMPREDIKSI FINANCIAL DISTRESS DENGAN BINARY LOGIT REGRESSION PERUSAHAAN TELEKOMUNIKASI 2019THE INFLUENCE OF LIQUIDITY, CAPITAL STRUCTURE, PROFITABILITY AND CASH FLOWS ON THE COMPANY’S FINANCIAL DISTRESS 2015Assessing the State of Financial Distress in Listed Companies in Ghana: Signs, Sources, Detection and Elimination – A Test of Altman’s Z-Score 2020The Analysis of Fundamental Variables and Macro Economic Variables in Predicting Financial Distress

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