applying variable deletion strategies in bankruptcy studies to capture common information and increase their reality

in financial distress studies selection of variable is commonly basedon the success of variables in variable sets employed in earlierbankruptcy studies, suggestions in the literature or an accompanyingdata reduction in a large set of variables. if seemingly different variablesets exhibit a strong relationship then heterogeneous variable setscapture common information. canonical correlation analysis appropriatelyexamines the relationship between two sets of measured variables.the main purpose of the present study was to illustrate the value ofvariable deletion strategies in canonical correlation analysis for moreparsimonious to capture common information. in research contents, thelaw of parsimony states that the fewer variables used to explain asituation, the more probable that the explanation will be closer to reality.therefore, as variable sets become more parsimonious there are greaterprobabilities that the results of the analysis will be replicable. todetermine the common information between variable sets in financialdistress studies, the study selected two specific bankruptcy models:altman, the most famous model, and deakin, the biggest model. theresults indicated that as the number of variables increase, the probableeffect of these sources of error variation on the canonical correlationincreases. therefore, the goal of a variable deletion strategy is toestimate as much variance with the smallest variable set possible. in thisstudy the goal was achieved by removing the three variables in variablesets employed in selected bankruptcy studies.