Large Language Models for Predicting Investor Sentiment and Market Behavior Based on Self-Narrative Analysis
Keywords:
large language models, investor sentiment, self-narrative analysis, market behavior, financial NLP, system architecture, fairness, governanceAbstract
The prediction of investor sentiment and its influence on market dynamics has long challenged both financial economists and computational researchers. Traditional sentiment analysis tools, while effective at capturing coarse affective signals, often fail to uncover the layered cognitive and motivational structures embedded in the narratives that investors construct about themselves and their economic environments. This paper argues that large language models (LLMs) open a transformative pathway for extracting such self-narrative signals and translating them into actionable predictions of market sentiment and aggregate behavior. We adopt a system-level perspective that moves beyond algorithmic novelty to examine the architectural trade-offs, data governance frameworks, infrastructure design, and fairness considerations essential for deploying LLM-based sentiment prediction in real-world financial ecosystems. The analysis integrates insights from behavioral finance, narrative psychology, and sociotechnical systems engineering to delineate how self-narrative features—ranging from future-oriented planning statements to justificatory reasoning—can serve as leading indicators of market shifts. We further explore the deployment challenges associated with latency-sensitive trading environments, the sustainability implications of large-scale model inference, and the regulatory pressures shaping the transparency and explainability of such systems. By foregrounding structural robustness, epistemic uncertainty quantification, and fairness across market participants, we provide a comprehensive blueprint for the responsible integration of LLM-driven narrative analytics into institutional investment processes. The paper concludes with a discussion of the broader policy ramifications, including the potential for systemic risk amplification and the necessity for co-designed governance mechanisms that align technological capabilities with market stability objectives.
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