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A Hybrid Generative-Discriminative Framework For Tactical Asset Management Using LLM-Infused Time Series Forecasting by Tonny Njuguna is a document available to read on EtoBox.

This thesis introduces TAM-Clio, a hybrid framework combining Large Language Models (LLMs) and advanced time series forecasting to enhance tactical asset management. It addresses the limitations of current algorithmic trading systems by integrating qualitative insights from LLMs with quantitative data, aiming to improve predictions of asset volatility and market direction. The research proposes a novel methodology that incorporates LLM-generated metrics into a discriminative time series model, with the goal

Author

Tonny Njuguna

Language

EN