Sentiment Analysis of Financial News and Its Influence on Stock Market Behavior

Abstract

The integration of natural language processing and deep learning architectures into financial market ecosystems has transformed the velocity, scale, and nature of asset pricing and capital allocation. This paper examines the socio-technical architecture, systemic trade-offs, and structural governance challenges underlying automated financial news sentiment analysis and its subsequent impact on stock market behavior. Rather than viewing sentiment extraction merely as an algorithmic optimization problem, we contextualize it within large-scale data infrastructures, real-time deployment constraints, and policy frameworks. We trace how unstructured textual data flows from global journalistic networks through complex semantic pipelines to influence high-frequency and algorithmic trading engines. This transmission mechanism alters market microstructures, shifting liquidity patterns and compounding systemic volatility through feedback loops. Furthermore, the paper addresses the technical and ethical trade-offs inherent in model deployment, analyzing how the quest for latency optimization often compromises semantic robustness and introduces cognitive biases into market operations. We scrutinize the infrastructural dependencies of modern language models, emphasizing the sustainability costs, computational footprints, and data governance vulnerabilities associated with institutional-grade financial monitoring. Finally, we evaluate the regulatory and policy dimensions required to mitigate market manipulation, semantic monoculture, and algorithmic cascading failures. By synthesizing perspectives from computational linguistics, financial engineering, and macro-policy governance, we present a comprehensive framework for designing resilient, fair, and stable socio-technical market infrastructures capable of navigating the complex realities of automated sentiment-driven economies.