Author(s)

Nicolas Emanuel Monti, Roberto Mariano Garcia

.

The aim of this study is to apply the statistical inference to identify if a firm is likely to become financially distressed in the short term. To do this, we decided to collect data from the firms’ financial statements. The analyses performed were based on a group of 45 financial ratios observed from a sample of 86 firms operating in Argentina. First, we used the principal component analysis to turn the information in the 45 original ratios into two new global variables named as ?Risk and ?Return. In this way, we can easily represent and compare in a graph the firms’ risk and return variations. By the computation of these new variables it is possible to quickly financially categorize a certain firm based on the risk the company has with regard to the nature of its business and the risk involved in the amount of debt it has taken in comparison to the profits that were generated during the last two fiscal years. Second, we performed a logistic regression analysis to estimate the probability that a firm becomes financially distressed in the short term. The model finally selected managed to successfully identify 85% of the companies from the sample and it explains 65% of the total sample variability. The model is represented by the following variables: 1) Current Debt Ratio, 2) Total Cost of Debt, 3) Operating Profit Margin, and 4) ?ROE. The outcomes from this study are two tools that were developed based on the statistical inference from which we can quickly asses the financial status of a firm based on its risks and return’s variation as well as to estimate the probability that a firm becomes financially distressed in the short term. There are different ways of taking these tools into practice such as: 1) to control and follow up the financial performance of a company, 2) to support the decision of lending money to a company, 3) to support the decision of investing money or the decision of merging with a company, 4) to support market analysis from a financial perspective, and 5) to support actions or decisions related to the financial assessment of a company that declares itself to be financially distressed.

Financial Distress, Financial Risk, Principal Component Analysis, Logistic Regression Analysis

N. Monti and R. Garcia, "A Statistical Analysis to Predict Financial Distress," Journal of Service Science and Management, Vol. 3 No. 3, 2010, pp. 309-335. doi: 10.4236/jssm.2010.33038.