A novel Boltzmann probability-based framework for predicting bank credit rating transitions

This study proposes a novel probabilistic framework for predicting bank credit rating transitions using a Boltzmann probability-based approach integrated with entropy and CRITIC-based weighting mechanisms. Credit rating transitions are traditionally modelled using Markov chains, which capture state transitions through a transition probability matrix. However, Markov chain models often fail to accommodate asymmetric transitions and economic fluctuations. To address this, our model introduces a percentile-based threshold mechanism within the Boltzmann probability framework, establishing dynamic upgrade and downgrade boundaries. The proposed methods (i.e.) based on entropy-driven and CRITIC-driven weights aim to capture the dynamic variability and interdependence of financial indicators. Boltzmann probabilities, derived from inverse composite energy scores, quantify the likelihood of credit rating stability, while the percentile-based threshold attempts to distinguish stable, upgrade, and downgrade states. Empirical validation using a bank dataset demonstrates the model’s predictive accuracy (in terms of a state change), outperforming traditional Markov approaches in capturing rating dynamics. This probabilistic methodology offers significant implications for credit risk management and financial stability assessment.