Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE Rankings

Many models have been explored for financial distress prediction, but no consistent conclusions have been drawn on which method shows the best behavior when different performance evaluation measures are employed. Accordingly, this paper proposes the integration of the ranking scores given by two popular multiple-criteria decisionmaking tools as an important step to help decision makers in selecting the model(s) properly. Selection of the most appropriate prediction method is here shaped as a multiple-criteria decision-making problem that involves a number of performance measures (criteria) and a set of techniques (alternatives). An empirical study is carried out to assess the performance of ten algorithms over six real-life bankruptcy and credit risk databases. The results reveal that the use of a unique performance measure often leads to contradictory conclusions, while the multiplecriteria decision-making techniques may yield a more reliable analysis. Besides, these allow the decision makers to weight the relevance of the individual performance metrics as a function of each particular problem.