Industry-Specific Models

This subsection presents the results for the Industry-Specific models for each of the five industries, using LR, MDA, and ANNs. The Energy industry’s results are showcased first, followed by the Financials industry, the Health industry, the Industrials industry, and finally, the IT industry. As mentioned earlier, the classification tables and figures are only presented for the industry-wide models, Table 3.6 will showcase the empirical results for all constructed models.

3.5.2.1 Energy

 LR Model: The model initially contained 17 independent variables – refer to the Methodology section for list of variables. Six independent variables made a statistically significant contribution to the model (Total Revenue, EBIT, Total Equity, ROE, Enterprise Value, and Cash per Share). The full model containing all predictors was statistically significant, χ² (17, N = 148) = 53.33, p < .001, indicating that the model was able to distinguish between companies that are listed as failed or successful. The model as a whole explained between 30.3%

(Cox and Snell R Square) and 41.2% (Nagelkerke R Squared) of the variance in company status, and correctly classified 77.7% of cases.

 MDA Model: The Industry-Specific MDA model for the Energy industry yielded a result of 72.2% for the original grouped cases that were correctly classified, and after cross-validation that result fell to 66.5%. This result is better than all the results out of all the Industry-Wide models. Only one independent variable made a unique statistically significant contribution to the model, namely: ‘Cash per Share’.

 ANN Model: The Industry-Specific ANN model for the Energy industry yielded an overall classification accuracy result of 82.7%. As for variable importance, the top three variables that had the greatest predictive power in shaping this model were ‘Cash per Share, ‘PER’, and ‘Share Price/Cash Flow’. Refer to Discussion section for rationale of variable importance.

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3.5.2.2 Financials

 LR Model: The model initially contained 16 independent variables. Three independent variables made a unique statistically significant contribution to the model (ROA, EV/EBITDA, and Current Ratio). The full model containing all predictors was statistically significant, χ² (16, N = 166) = 30.86, p < .02, indicating that the model was able to distinguish between companies that are listed as failed or successful. The model as a whole explained between 17%

(Cox and Snell R Square) and 23% (Nagelkerke R Squared) of the variance in company status, and correctly classified 66.3% of cases.

 MDA Model: The Industry-Specific MDA model for the Financials industry yielded a result of 54.9% for the original grouped cases that were correctly classified, and after cross-validation that result fell to 49.2%. This result is only slightly better than the industry-wide MDA model, but it is still largely an unencouraging result. Only two independent variables made a unique statistically significant contribution to the model, namely: ‘PER’ and ‘Gross Gearing’.

 ANN Model: The Industry-Specific ANN model for the Financials industry yielded a classification accuracy average of 63.8%. As for variable importance, the top three variables that had the greatest predictive power in shaping this model were ‘Gross Debt per Cash Flow’, ‘Cash per Share’, and ‘Current Ratio’.

Refer to Discussion section for rationale of variable importance.

3.5.2.3 Health

 LR Model: The model contained 17 independent variables. Two independent variables made a unique statistically significant contribution to the model (EBIT and Quick Ratio). The full model containing all predictors was statistically significant, χ² (16, N = 166) = 30.86, p < .01, indicating that the full model containing all predictors was statistically significant, χ² (17, N = 149) = 22.8, p

< .05, indicating that the model was able to distinguish between companies that are listed as failed or successful. The model as a whole explained between

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14.2% (Cox and Snell R Square) and 19.6% (Nagelkerke R Squared) of the variance in company status, and correctly classified 69.8% of cases.

 MDA Model: The Industry-Specific MDA model for the Health industry yielded a result of 65.6% for the original grouped cases that were correctly classified, and after cross-validation that result fell to 56.4%. This result is slightly better than the industry-wide MDA model, but it is still largely an unencouraging result.

Only two independent variables made a unique statistically significant contribution to the model, namely: ‘Cash/Share’ and ‘Current Ratio’.

 ANN Model: The Industry-Specific ANN model for the Health industry yielded a classification accuracy of 75.50%. As for variable importance, the top three variables that had the greatest predictive power in shaping this model were

‘Gross Debt per Cash Flow’, ‘Current Ratio’, and ‘Gross Gearing’. Refer to Discussion section for rationale of variable importance.

3.5.2.4 Industrials

 LR Model: The model contained 18 independent variables. One independent variable made a unique statistically significant contribution to the model (Current Ratio). The full model containing all predictors was statistically significant, χ² (18, N = 188) = 21.07, p < .02, indicating that the model was able to distinguish between companies that are listed as failed or successful. The model as a whole explained between 10.3% (Cox and Snell R Square) and 14% (Nagelkerke R Squared) of the variance in company status, and correctly classified 66% of cases.

 MDA Model: The Industry-Specific MDA model for the Industrials industry yielded a result of 60.2% for the original grouped cases that were correctly classified, and after cross-validation that result fell to 53.8%. This result is slightly better than the industry-wide MDA model, but it is still largely an unencouraging result. Three independent variables made a unique statistically significant contribution to the model, namely: ‘Enterprise Value’, ‘PER, and

‘Cash/Share’.

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 ANN Model: The Industry-Specific ANN model for the Industrials industry yielded an average classification accuracy result of 69.10%. As for variable importance, the top three variables that had the greatest predictive power in shaping this model were ‘Gross Debt per Cash Flow’, ‘Gross Gearing’, and

‘Current Ratio’. Refer to Discussion section for rationale of variable importance.

3.5.2.5 IT

 LR Model: The model contained 17 independent variables. Six independent variables made a unique statistically significant contribution to the model (Total Equity, ROE, Enterprise Value, Gross Gearing, PER, and Debt/CF). The full model containing all predictors was statistically significant, χ² (17, N = 152) = 48.48, p < .001, indicating that the model was able to distinguish between companies that are listed as failed or successful. The model as a whole explained between 27.3% (Cox and Snell R Square) and 36.5% (Nagelkerke R Squared) of the variance in company status, and correctly classified 75% of cases.

 MDA Model: The industry-specific MDA model for the IT industry yielded a result of 59.7% for the original grouped cases that were correctly classified, and after cross-validation that result fell to 50.3%. This result is slightly better than the industry-wide MDA model, but it is still largely an unencouraging result. Only one independent variable made a unique statistically significant contribution to the model, namely: ‘Enterprise Value’.

 ANN Model: The Industry-Specific ANN model for the IT industry an average classification accuracy result of 58.00%. As for variable importance, the top three variables that had the greatest predictive power in shaping this model were ‘Gross Gearing’, ‘Cash/Share’, and ‘Current Ratio’. Refer to Discussion section for rationale of variable importance.

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