Corporate Failure Prediction: A Fresh Technique for Dealing Effectively With Normality Based On Quantitative and Qualitative Approach

Corporate Failure Prediction: A Fresh Technique for Dealing Effectively With Normality Based On Quantitative and Qualitative Approach

Author(s) by Alhassan Bunyaminu, Shani Bashiru
Pages 1-12
Keywords Solvency, Generalized Linear Modelling, Factor Analysis, Financial Ratio, quantitative models, qualitative analysis
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Abstract


This study uses a combination of quantitative and qualitative models to predict business failure with an appreciable degree of accuracy and/or precision. Quantitatively, the study used Factor Analysis (FA) to reduce the dimensionality of the data and further employed the Generalised Linear Modelling (GLM) technique which skips and/or relaxes the use of the normality assumption test that must be used by the General linear models. Qualitatively, the study adopted the most notable qualitative A- score model of Argenti (1976), which suggests that business failure process follows three predictable sequences: Defects, Mistakes made and Symptoms of failure. Among the three link functions (models) of GLM, the Logit model provides the highest overall accuracy rate with the lowest Akaike Information Criteria (AIC): 49.484. Regarding the 19 corporate determinants classified into 5 distinct categories, namely: Profitability and Employee Efficiency, Leverage and Liquidity, Asset utilization, Growth ability and Size, the significant variables that have appeared as a consistent indicator of financially distressed companies in the best model (logit) are Profitability ratio (Return on total assets) and Leverage ratio (Solvency, Gearing and Interest cover).In terms of the qualitative analysis, it was revealed that organizations that are prone and susceptible to corporate failure display high scores in defects usually in the range of 40 which is a high rate in the scale of 43 (highly unsatisfactory). As far as the three main mistakes are concerned (high gearing, overtrading and the big project) which failed companies exhibit, high gearing had a higher score of 15 which validates the findings in the quantitative analysis. Among symptoms of failure (Financial signs, Creative accounting, Non-financial signs – various signs include frozen management salaries, delayed capital expenditure, Terminal signs – at the end of the failure process, the financial and non-financial signs become so obvious and debilitating that even the casual observer recognises them), Financial signs holds sway posting a higher score of (16).

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