Advancing Quantamental Investment Frameworks through Large Language Model Reasoning Integrating Textual Sentiment Volatility and Structured Financial Statement Analytics

Abstract

The evolution of asset management has increasingly converged toward the "quantamental" approach, a paradigm that seeks to synthesize the systematic rigor of quantitative modeling with the nuanced, discretionary depth of fundamental analysis. Despite significant advancements in computational finance, traditional models often struggle to reconcile high-frequency unstructured data, such as news sentiment and social media volatility, with the periodic, structured nature of corporate financial statements. This research paper proposes an advanced architectural framework for quantamental investment that leverages the reasoning capabilities of Large Language Models (LLMs) to bridge this cognitive gap. By treating LLMs not merely as sentiment extractors but as sophisticated reasoning engines capable of multi-modal data synthesis, the proposed system integrates textual sentiment volatility with longitudinal financial statement analytics. The discussion focuses on the system-level design, examining the structural trade-offs between computational latency and reasoning depth, the governance of algorithmic decision-making, and the socio-technical implications of deploying autonomous reasoning agents within global financial infrastructures. We further explore the robustness of these systems against data poisoning and hallucination, the sustainability of large-scale inference in financial contexts, and the policy frameworks required to ensure market fairness and systemic stability. Through extensive conceptual analysis and systemic evaluation, this study demonstrates that the integration of generative reasoning into quantitative pipelines facilitates a more holistic understanding of market dynamics, ultimately enhancing the resilience and interpretability of modern investment strategies in an increasingly complex digital economy.