This project presents the application of a MS-QRNN model designed to estimate Value at Risk accurately by integrating both numerical financial time-series data and textual data. The model incorporates NLP techniques, including FinBERT for textual analysis, and Neural Network architectures to predict the quantiles of asset return distributions.
