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Predictability of Predictors of Corporate Failure Using Forward Logistic Regression: Evidence from Bangladesh

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Listed:
  • Md. Azim

    (University of Dhaka Ringgold standard institution, Bangladesh)

  • Md. Jamil Sharif

    (University of Dhaka Ringgold standard institution, Bangladesh)

Abstract

The purpose of this study is to find out the financial variables that predominantly influence the prediction of corporate failure. The study is based on forward logistic regression. The variables of the Altman Model have been utilized to test the predictability of predictors of corporate failure because this model is widely employed in this context. A total of 217 firm years of data from 2007 to 2019 have been utilized for this study. The findings of the study show that among the variables considered in this study, the ratio of Earnings before Interest & Taxes to Total Assets has the most predictive capability to predict corporate failure. Besides, the probability of failure can also be better predicted collectively by Net Working Capital Ratio, Equity-to-Liability Ratio, & Asset Turnover Ratio along with the Earnings before Interest and Taxes to Total Assets Ratio. An important finding indicates that the Retained Earnings to Total Assets Ratio is not an effective predictor when it comes to forecasting corporate failure. Through the application of Forward Logistic Regression, one can identify the most influential variables for predicting corporate failure. The decision makers can utilize the findings to identify the factors that possess the highest capability in forecasting corporate failure, thereby enabling them to take the necessary preventive measures.

Suggested Citation

  • Md. Azim & Md. Jamil Sharif, 2025. "Predictability of Predictors of Corporate Failure Using Forward Logistic Regression: Evidence from Bangladesh," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 445-461, June.
    • Handle: RePEc:nwe:eajour:y:2025:i:2:p:445-461
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    References listed on IDEAS

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