Daniel Abib

December 1, 2023by Daniel Abib0

We had a great time attending the 2023 INFORMS Annual Meeting that took place between October 15th to October 18th in Phoenix, AZ. This is one of the largest conferences in the field of OR, with 6,000+ attendees and 1,400+ sessions.

The BAL had a strong presence at the conference with four presentations:

  • On October 15th, we had two presentations:
    • Cristián Bravo presented the work with Kamesh Korangi and Christophe Mues on “Large-Scale Portfolio Optimization using Graph Attention Networks”.


  • Daniel Abib presented the work with Cristián Bravo, Raffaella Calabrese, and María Óskarsdóttir on “Optimal Feature Split in Classification Models with Dependency”.


  • On October 17th, Sahab Zandi presented the work with Kamesh Korangi, María Óskarsdóttir, Christophe Mues, and Cristián Bravo on “Leveraging Dynamic Multilayer Networks for Modelling Credit Risk Contagion in SMEs”.


  • On October 18th, Yuhao (Jet) Zhou presented the work with Collins Ntim, María Óskarsdóttir, Matthew Davison, and Cristián Bravo on “Uncovering the Network Power Gap: A Deep Learning Approach to Investigating Gender Disparities in the Boardrooms of Canadian Public Firms”.


We also had fun trying a few good restaurants in Scottsdale, AZ!

This was a great chance to showcase the preprints that will come out in the next few months. Stay tuned for them!

June 28, 2023by Daniel Abib0

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

April 16, 2023by Daniel Abib0

On March 21 the director of BAL, prof. Cristián Bravo, participated on The Shift With Shane Hewitt podcast, discussing  recent collapses of Sillicon Valley Bank and Credit Suisse, as well as the current issues of the banking sector.

Listen to the podcast here.