Applying Variable Deletion Strategies in Bankruptcy

In financial distress studies selection of variable is commonly based on the success of variables in variable sets employed in earlier bankruptcy studies, suggestions in the literature or an accompanying data reduction in a large set of variables. If seemingly different variable sets exhibit a strong relationship then heterogeneous variable sets capture common information. Canonical correlation analysis appropriately examines the relationship between two sets of measured variables. The main purpose of the present study was to illustrate the value of variable deletion strategies in canonical correlation analysis for more parsimonious to capture common information. In research contents, the law of parsimony states that the fewer variables used to explain a situation, the more probable that the explanation will be closer to reality. Therefore, as variable sets become more parsimonious there are greater probabilities that the results of the analysis will be replicable. To determine the common information between variable sets in financial distress studies, the study selected two specific bankruptcy models: Altman, the most famous model, and Deakin, the biggest model. The results indicated that as the number of variables increase, the probable effect of these sources of error variation on the canonical correlation increases. Therefore, the goal of a variable deletion strategy is to 30 / Applying Variable Deletion Strategies in Bankruptcy Studies to… estimate as much variance with the smallest variable set possible. In this study the goal was achieved by removing the three variables in variable sets employed in selected bankruptcy studies.