Research on the Development and Optimization of Financial Investment in the Context of Big Data
Main Article Content
Keywords
big data, financial investment, DeepSeek, artificial intelligence, optimization pathway
Abstract
With the deep integration of big data and artificial intelligence technologies, the field of financial investment is undergoing a profound paradigm shift. This study takes the practical performance of the domestic large model DeepSeek in the “Alpha Arena” AI Trader Competition as an example, and through literature review and empirical analysis, systematically explores the technical logic and optimization pathways of intelligent financial investment. The study finds that the DeepSeek model, by integrating multi-source heterogeneous data, employing nonlinear deep learning algorithms, and implementing dynamic risk control mechanisms, has achieved a fundamental transformation of investment strategies from experience-driven to data-intelligence-driven. Based on this, the present study proposes a five-dimensional optimization framework encompassing data factor circulation, enhancement of algorithm transparency, transformation of market structure, reconstruction of regulatory adaptability, and full-cycle risk management, aiming to provide theoretical support and practical guidance for the digital transformation of financial institutions.
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