By Mahsa Tavakoli @Bal:

Our research study was undertaken with the aim of enhancing the accuracy of predicting company credit ratings, a critical factor in evaluating their financial stability. Unlike previous studies that solely focused on structured data, such as numbers and figures, we recognized the significance of incorporating other, non-structured information. Thus, our primary objective was to evaluate the effectiveness of employing advanced deep learning models to merge both structured and unstructured data, particularly textual information. Through this approach, we sought to provide a more comprehensive analysis and improve the overall predictive capabilities of the models. In our quest for the optimal approach, we conducted thorough testing of various fusion strategies and deep learning models, including CNN, LSTM, GRU, and BERT. To our surprise, we discovered that a CNN-based model (Figure 2), which effectively amalgamated data from diverse sources, outperformed more intricate models. Leveraging this model enabled us to achieve highly precise credit rating predictions.

Furthermore, we delved into the contribution of different data types to these predictions. Textual data, such as insights shared by company managers, played a pivotal role, particularly during challenging periods like the COVID-19 pandemic. This underscored the significance of contextual information and managerial perspectives in accurately predicting credit ratings.

Additionally, our research encompassed a comparative analysis of ratings provided by various agencies. Moody’s credit ratings emerged as the frontrunner, surpassing those of other agencies like Standard & Poor’s and Fitch Ratings, especially in medium-term predictions.

Collectively, our research provides a comprehensive framework that empowers rating agencies and financial institutions to make well-informed decisions when assigning credit ratings. By incorporating a combination of structured and unstructured data and leveraging the most effective deep learning models, they can significantly enhance the precision of credit rating predictions, thereby augmenting their overall decision-making process.

Fig1: Blending Textual Managers’ Insights and Companies Numerical Data for Precise Credit Rating Predictions

Fig2: Architecture of the CNN ensemble for the best model, showing
the convolution and dropout layers with two streams of data that includes
text and numerical data (N1, N2, N3, N4).

Link to the working paper: https://arxiv.org/abs/2304.10740