Financial Distress Prediction: Empirical Evidence From Indian Automobile Companies

S. Poornima; Theivanayaki M

doi:10.26703/jct.v7i1.312

Authors

Dr. S. Poornima Assistant Professor, Department of Business Management, PSGR Krishnammal College for Women, Coimbatore
Theivanayaki M. Research Scholar, Department of Business Management, PSGR Krishnammal College for Women, Coimbatore

DOI:

https://doi.org/10.26703/jct.v7i1.312

Keywords:

Discriminant Analysis, Ratio Analysis, Indian Automobile Companies

Abstract

Financial distress is of crucial importance in financial management especially in the case of competitive environment. Failure is not an impulsive outcome and it grows constantly in stages. A spontaneous protective effort could be accommodated if the company is anticipated to be proceeding in the direction of potential bankruptcy and this can help alleviate the financial distress to all investor and decrease the costs of bankruptcy. This study extends a failure prediction model for Indian Automobile companies. This study hopes to accommodate some important results relevant to authorities and stake holders. The capability to detect potential financial problems at a premature stage is absolutely essential because it helps to ensure business, financial, economic and political environment stability. The results show good performance with a highly correct categorization factuality rate of more than 90%. Eight ratios were determined significant out of 38 financial ratios utilized in this analysis to discriminate among failed and non- failed companies. The significant variables are Operating margin (%), Gross profit margin (%), Return on long term funds (%), Total debt/equity, Cash earnings retention ratio, Exports as percent of total sales, Import companies in raw material consumed, Bonus component in equity capital (%)

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References

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END NOTES:

One way classification ANOVA is applied to each ratio to find the significant difference between top performing and least performing companies.

Discriminant analysis is done to all ratios under step-wise method to find those ratios that contributes to the discrimination.

Exports as percentage of total sales is not significant for actual data but after filtration it is significant

Correlation analysis was implemented on the variables that were normal under the normality tests. Variables with negative value were eliminated from the analysis. But variable those are statistically normal but with low significance level were incorporated.

Selected variable and associated variable that are highly correlated with it will not incorporate in a similar group and will create other group for following analysis. The optimum group with elevated achieves ratio will be selected as the final independent variables.