To Get Started: The Context

To Get Started: The Context

To begin to answer some of these important questions, we conducted a study of the financial vital signs of bankrupt and healthy companies. We first identified 66 failed firms from a list provided by Dun and Bradstreet. These firms were in manufacturing or retailing and had financial data available on the Compustat Research tape. Bankruptcy occurred somewhere between 1970 and 1982.

For each of these 66 failed firms, we selected a healthy firm of approximately the same size (as measured by the book value of the firm’s assets) from the same industry (3 digit SIC code) as a basis of comparison. This matched sample technique was used to minimize the impact of any extraneous factors (such as industry) on the conclusions of the study.

The study was designed to see how well bankruptcy can be predicted 2 years in advance. A total of 24 financial ratios were computed for each of the 132 firms using data from the Compustat tapes and from Moody’s Industrial Manual for the year that was 2 years prior to the year of bankruptcy. The table lists the 24 ratios together with an explanation of the abbreviations used for the fundamental financial variables. All these variables are contained in a firm’s annual report with the exception of CFFO. Ratios were used to facilitate comparisons across firms of various sizes.

Predicting Corporate Bankruptcy: Financial Variables and Ratios

What This Table Explains

The first four ratios using CASH in the numerator might be thought of as measures of a firm’s cash reservoir with which to pay debts. The three ratios with CURASS in the numerator capture the firm’s generation of current assets with which to pay debts. Two ratios, CURDEBT/DEBT and ASSETS/DEBTS, measure the firm’s debt structure. Inventory and receivables turnover are measured by COGS/INV and SALES/REC, and SALES/ASSETS measures the firm’s ability to generate sales. The final 12 ratios are asset flow measures.

Using This Data and R, Your Job Is To:

• Decide on what data mining technique(s) would be appropriate in assessing whether there are groups of variables that convey the same information and how important that information is? Conduct such an analysis.
• Comment in your presentation on the distinct goals of profiling the characteristics of bankrupt firms versus simply predicting (black box style) whether a firm will go bankrupt and whether both goals, or only one, might be useful. Also comment on the classification methods that would be appropriate in each circumstance.
• Explore the data to gain a preliminary understanding of which variables might be important in distinguishing bankrupt from nonbankrupt firms. (Hint: As part of this analysis, use side-by-side boxplots, with the bankrupt/not bankrupt variable as the x variable.)
• Using your choice of classifers, use R to produce several models to predict whether or not a firm goes bankrupt, assessing model performance on a validation partition. Based on the above, comment on which variables are important in classification, and discuss their effect.