Re-constructing the interbank links using machine learning techniques. An application to the Greek interbank market

• Published in: Intelligent systems with applications 2021-11, Vol.12, p.200055, Article 200055
• Main Authors: Petropoulos, Anastasios, Siakoulis, Vasilis, Lazaris, Panagiotis, Chatzis, Sotirios
• Format: Article
• Language: English
• Subjects: Analysis; C53; Crises forecasting; G01; G21; Machine learning; Networks; Sentiment; Text mining
• ISSN: 2667-3053; 2667-3053
• DOI: 10.1016/j.iswa.2021.200055

•We forecast the probability of a pair of banks entering into an interbank market borrower - lender relationship considering their financial characteristics and their past observed behavior.•Our purpose is to depart from agnostic assumptions usually employed in interbank matrix allocation algorithms and take into account the financial features of the banks when assigning prior link probabilities.•Our main finding is that machine learning algorithms outperforms the benchmark logistic regression model in interbank link forecasting. We propose an innovative approach to model the probability of interlinkages in an interbank network with the use of Machine Learning techniques. More precisely we forecast the probability of a pair of banks entering into an interbank market borrower - lender relationship considering their financial characteristics and their past observed behavior. In this framework we examine a new method that employs machine learning in order to increase the accuracy of agnostic algorithms in reconstructing a financial network. The XGBOOST method is combined with both Maximum Entropy (MAXE) and Minimum Density (ANAN). The main contribution of this paper is that we enrich the information generally available for financial networks with variables that are available for the publication of banks financial statements (ensemble method). A set of agnostic models, i.e. models that the exposure allocation algorithm does not include prior information, are used as a benchmark to measure the additional benefit for applying machine learning in estimating prior network probabilities. By comparing the results between the agnostic algorithms and the ensemble method we see an increase in the accuracy and a decrease in the MAE of the financial networks on average. Our purpose is to depart from agnostic assumptions usually employed in interbank matrix allocation algorithms and take into account the financial features of the banks when assigning prior link probabilities. Our main finding is that machine learning algorithms outperforms the benchmark Logistic Regression model in interbank link forecasting and this is also reflected in the enhanced performance when overall network similarity measures are performed.