The significant impact of artificial intelligence on asset pricing

Abstract

This paper primarily discusses the development of asset pricing theory, tracing its evolution from Markowitzs Portfolio Theory and the Capital Asset Pricing Model (CAPM) to multi-factor models. It highlights that traditional models, due to their simplifying assumptions and limited computational capacity, struggle to cope with highly dynamic and nonlinear real-world markets, particularly in the context of major event shocks, the utilization of unstructured information, and complex investor behavior. Building on this, the paper focuses on the transformative impact of artificial intelligence (AI) technologies, such as machine learning and natural language processing, on asset pricing. AI enhances forecasting accuracy, enables real-time responses to market changes, and extracts insights from multi-source unstructured data, thereby significantly improving pricing efficiency and risk management capabilities. Finally, the paper emphasizes the need for continuous advancement in areas such as algorithm optimization, data quality, regulatory compliance, and humanmachine collaboration to achieve more effective applications of AI in asset pricing.

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