The Paradigm Shift in Financial Intelligence: From High-Frequency Data Microstructure to Semantic Knowledge Representation
Keywords:
Financial Intelligence, Semantic Knowledge Representation, High-Frequency Data, Market Analysis, Decision-MakingAbstract
This research article investigates the paradigm shift in financial intelligence, transitioning from traditional high-frequency data microstructure analysis to advanced semantic knowledge representation. The study explores the limitations of current microstructural approaches and introduces a novel framework for integrating semantic technologies to enhance financial decision-making. A hybrid methodology combining quantitative data analysis and semantic modeling is employed, with experiments conducted on financial datasets to evaluate the proposed approach. Results demonstrate significant improvements in interpretability, scalability, and predictive accuracy. The discussion highlights the implications of semantic representation for financial intelligence, including its potential to redefine market analysis and risk assessment. The article concludes with insights on future research directions and the transformative impact of semantic technologies in finance.References
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