Large Language Models for Financial Sentiment Analysis and Market Intelligence
Keywords:
large language models; financial sentiment analysis; market intelligence; financial natural language processing; system governance; market surveillanceAbstract
Large language models have introduced a different computational foundation for financial sentiment analysis and market intelligence. This paper examines the integration of transformer-based language models into financial text processing from a systems perspective, focusing on architectural choices, data infrastructure, deployment constraints, and organizational governance. The discussion compares domain-adapted encoders with general-purpose generative models, explores the mediation between unstructured market communications and investment decision support, and analyzes the structural trade-offs between predictive expressiveness, interpretability, and operational stability. It further addresses regulatory compliance, privacy preservation, bias management, and the environmental and financial sustainability of model operations. The paper argues that the value of large language models in finance depends not only on language modeling accuracy but also on the quality of surrounding socio-technical infrastructure, including data provenance, evaluation protocols, monitoring, and accountability mechanisms. A central theme is that financial sentiment analysis should be treated as a high-assurance system rather than an isolated predictive task, requiring layered safeguards, human oversight, and alignment with market conduct rules. The paper synthesizes recent research across natural language processing, finance, and policy, and proposes directions for robust, fair, and sustainable deployment of language-based market intelligence systems.
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