2013•Unpublished venueRequires access

The use of financial ratios in predicting corporate failure in Sri Lanka

A. M. I. Lakshan, Sri Santhi Lanka

Open publisher page 40 citations

Abstract

The purpose of this research is to develop a model using financial ratios to predict corporate failure of listed companies in Sri Lanka. This study utilized publicly available data from annual reports of a sample of 70 failed firms and a sample of matched 70 non failed firms listed on Colombo stock market for a period covering the 2002 to 2008 financial years with logistic regression analysis. A total of fifteen financial ratios were used as predictor variables of corporate failure. Analysis of the statistical testing results indicated that the prediction accuracy of the model consists with financial ratios is 77.86% one year prior to failure. Furthermore, predictive accuracy of the model in all three years prior to failure is above 72%. Hence model is robust in obtaining accurate results for up to three years prior to failure. Final model includes three financial ratios; working capital to total assets, debt ratio and cash flow from operating activities to total assets. These variables are having more explanatory power to predict corporate failure. Therefore, model developed in this study can assist investors, managers, shareholders, financial institutions, auditors and regulatory agents in Sri Lanka to forecast corporate failure of listed companies.

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What this paper is about

The purpose of this research is to develop a model using financial ratios to predict corporate failure of listed companies in Sri Lanka. This study utilized publicly available data from annual reports of a sample of 70 failed firms and a sample of matched 70 non failed firms listed on Colombo stock market for a period covering the 2002 to 2008 financial years with logistic regression analysis. A total of fifteen financial ratios were used as predictor variables of corporate failure. Analysis of the statistical testing results indicated that the prediction accuracy of the model consists with financial ratios is 77.86% one year prior to failure. Furthermore, predictive accuracy of the model in all three years prior to failure is above 72%. Hence model is robust in obtaining accurate results for up to three years prior to failure. Final model includes three financial ratios; working capital to total assets, debt ratio and cash flow from operating activities to total assets. These variables are having more explanatory power to predict corporate failure. Therefore, model developed in this study can assist investors, managers, shareholders, financial institutions, auditors and regulatory agents in Sri Lanka to forecast corporate failure of listed companies.

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Available abstract

The purpose of this research is to develop a model using financial ratios to predict corporate failure of listed companies in Sri Lanka. This study utilized publicly available data from annual reports of a sample of 70 failed firms and a sample of matched 70 non failed firms listed on Colombo stock market for a period covering the 2002 to 2008 financial years with logistic regression analysis. A total of fifteen financial ratios were used as predictor variables of corporate failure. Analysis of the statistical testing results indicated that the prediction accuracy of the model consists with financial ratios is 77.86% one year prior to failure. Furthermore, predictive accuracy of the model in all three years prior to failure is above 72%. Hence model is robust in obtaining accurate results for up to three years prior to failure. Final model includes three financial ratios; working capital to total assets, debt ratio and cash flow from operating activities to total assets. These variables are having more explanatory power to predict corporate failure. Therefore, model developed in this study can assist investors, managers, shareholders, financial institutions, auditors and regulatory agents in Sri Lanka to forecast corporate failure of listed companies.

Key concepts: Financial ratio, Sri lanka, Shareholder, Finance, Business, Logistic regression, Cash flow, Debt ratio

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