Knowledge Graph Construction for the U.S. Stock Market: A Statistical Learning and Risk Management Approach
DOI:
https://doi.org/10.70393/6a6374616d.323439ARK:
https://n2t.net/ark:/40704/JCTAM.v2n1a01Disciplines:
Artificial IntelligenceSubjects:
Statistical AnalysisReferences:
38Keywords:
Dynamic Knowledge Graphs (DKGs), Large Language Models (LLMs), Financial Market Analysis, Risk ManagementAbstract
This paper explores the integration of dynamic knowledge graphs (DKGs) and advanced AI techniques, such as large language models (LLMs) and graph neural networks (GNNs), for enhancing financial market analysis and risk management. By developing the Integrated Context Knowledge Graph Generator (ICKG) and the Financial Dynamic Knowledge Graph (FinDKG), the study demonstrates how these models can predict market trends, optimize investment strategies, and improve risk mitigation. The results highlight the superior performance of the KGTransformer model in link prediction tasks, showcasing its potential for more accurate and insightful financial decision-making.
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