LorenzoAusiello / Multi-Sources-Quantile-Regression-Neural-Network-in-QWIMLinks
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.
☆12Updated last year
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