Determination of weights for an optimal credit rating model based on default and nondefault distance maximization

The reasonableness of indicator weights is a key determinant of the reliability of a credit rating system. An unreliable credit rating system can lead to incorrect decisions on security investments, loan approvals and other relevant issues. Several combinations of weights are possible in such systems. Thus, it may be straightforward to determine a reasonable weight for a single indicator, but difficult to determine those for a group of indicators, and especially difficult to determine the optimal weights for such a group in a credit rating system. This study proposes a credit rating model that accurately identifies default and nondefault companies by maximizing intergroup credit score deviations and minimizing intragroup deviations. Further, this study establishes a model for determining the optimal weights for a set of indicators in a credit rating system. The empirical results show that the proposed optimal weight-based model outperforms four other weighting models (entropy weighting model, average variance weighting model, coefficient of variation weighting model and deviation weighting model). Lastly, the results show that nonfinancial indicators have a greater effect on the credit status of Chinese small and medium-sized enterprises than financial indicators do.