Enhancing Long Horizon Financial Forecasting via Retrieval Augmented Large Language Models Integrating Historical Temporal Patterns and Narrative Context
Keywords:
Financial Forecasting, Large Language Models, Retrieval-Augmented Generation, Socio-Technical Systems, Narrative Intelligence, Long Horizon Prediction, Algorithmic GovernanceAbstract
The evolution of financial forecasting has moved from linear statistical modeling to complex deep learning architectures, yet the challenge of long-horizon prediction remains significant due to the inherent volatility and non-stationarity of global markets. Conventional models often struggle with the "vanishing signal" problem where long-term temporal dependencies are lost amidst short-term noise. This paper proposes a system-level framework for enhancing long-horizon financial forecasting through the integration of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). By synthesizing historical temporal patterns with qualitative narrative context, such as earnings reports, geopolitical news, and regulatory shifts, the proposed architecture bridges the gap between quantitative price signals and qualitative market drivers. We explore the structural trade-offs involved in deploying such large-scale socio-technical infrastructures, focusing on the robustness of retrieval mechanisms, the computational sustainability of high-frequency LLM inference, and the governance requirements for algorithmic fairness in automated trading environments. The discussion extends to the deployment challenges in real-world financial systems, emphasizing the need for cross-domain data synchronization and the mitigation of hallucination risks in LLM-driven synthesis. Our analysis suggests that while the integration of narrative context significantly improves model interpretability and long-term accuracy, it necessitates a rigorous policy framework to manage systemic risks associated with automated sentiment amplification. This research provides a comprehensive roadmap for the next generation of financial intelligence systems that treat market data as a multi-modal narrative rather than an isolated numerical sequence.
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