LorenzoAusiello / Multi-Sources-Quantile-Regression-Neural-Network-in-QWIM

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.
11Updated 10 months ago

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