AI-Driven Multiscenario Interest Rate Forecasting in Banks: A Proof-of-Concept Prototype

Battle Count: 81

The paper is primarily focused on Asset-Liability Management (ALM) and interest rate risk management in banking rather than direct quantitative trading strategies. However, the BVAR-based yield curve forecasting, sentiment analysis of central bank communications, and scenario-based simulations are directly applicable to fixed-income trading, interest rate derivatives pricing, and macro-driven trading strategies. The multiscenario framework could inform position sizing, hedging decisions, and regime detection for rates traders. The sentiment analysis module provides early warning signals for monetary policy shifts relevant to trading. The relevance is moderate as the primary audience is bank risk managers and ALM desks rather than proprietary trading desks.

The system integrates multiple complex components: (1) NLP pipeline with SBERT, UMAP, HDBScan for topic modelling; (2) FinBERT-based sentiment analysis with few-shot learning and three-factor classification; (3) BVAR estimation via MCMC (Metropolis-Hastings) with hierarchical Bayesian priors in R; (4) Granger causality testing for variable selection; (5) Scenario simulation with probabilistic weighting; (6) Interactive Dash-based dashboard with multiple visualization modules; (7) Integration of Bloomberg, ECB, and internal bank data sources. The system requires expertise in econometrics, NLP, Bayesian statistics, and web application development. The modular architecture helps but the overall integration complexity is high.

The paper provides extensive R scripts in appendices (Granger tests, BVAR estimation for different yield regimes, backtesting VAR/ARIMA), uses open-source tools (R BVAR package, Python Dash, SBERT, UMAP, HDBScan, FinBERT), and documents hyperparameters. However, the underlying data comes from proprietary sources (Bloomberg, ECB, Refinitiv, EuroStat), the LLM integration uses an OpenAI-API-compatible provider without specifying the exact model, and the prototype is tested in a specific bank context. The modular architecture and documented code facilitate partial reproduction, but full replication requires access to proprietary financial data feeds.