Multimodal Financial Market Prediction Using Market Data and News Sentiment
Abstract
The evolution of financial market prediction has transitioned from univariate time-series analysis toward complex, multimodal architectures that synthesize structured market data with unstructured linguistic signals. This paper investigates the systemic integration of heterogeneous data streams—specifically high-frequency market metrics and global news sentiment—into a unified predictive framework. We argue that the efficacy of modern financial AI is not merely a product of algorithmic precision but is fundamentally contingent upon the engineering of robust socio-technical infrastructures. This research explores the structural trade-offs inherent in cross-modal fusion, addressing the tensions between model depth, inference latency, and interpretability. We further scrutinize the deployment requirements of these systems, emphasizing the physical high-performance computing infrastructure and the data governance protocols necessary to maintain institutional trust and market stability. Beyond technical performance, the paper addresses the critical dimensions of environmental sustainability in compute-heavy financial modeling, the ethical imperatives of fairness in sentiment appraisal, and the broader policy implications of widespread algorithmic convergence. By synthesizing perspectives from systems engineering, behavioral finance, and computational linguistics, this work provides a comprehensive roadmap for developing adaptive, transparent, and socially responsible multimodal forecasting systems. We conclude that while multimodal approaches offer unprecedented capacity for capturing market dynamics, their successful implementation requires a holistic approach to governance, infrastructure, and systemic robustness to safeguard the integrity of the global financial landscape.
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