Optimization Application and Risk Assessment of Artificial Intelligence Algorithms in Financial Asset Pricing Models
Abstract
Financial asset pricing is the core content of modern financial theories. The traditional pricing model is constrained by linear
hypothesis and static factors, with difficulty in adapting to the nonlinear, time-varying, and high-dimensional characteristics of financial
markets.AI algorithms, leveraging its advantages of nonlinear fitting, dynamic feature mining and adaptive learning, has provided a brand
new pathway for the optimization of asset pricing models. This article anchors in the core logic of financial asset pricing, analyzes the op
timization dimensions of AI algorithms for traditional pricing models, explores their value realization in practical application, concurrently
systematically identify and categorize risk derived from algorithmic application, and proposes targeted risk management strategies, aiming
to provide theoretical reference and practical guidelines for the in-depth implementation and standardized application of AI technology in
financial asset pricing.
hypothesis and static factors, with difficulty in adapting to the nonlinear, time-varying, and high-dimensional characteristics of financial
markets.AI algorithms, leveraging its advantages of nonlinear fitting, dynamic feature mining and adaptive learning, has provided a brand
new pathway for the optimization of asset pricing models. This article anchors in the core logic of financial asset pricing, analyzes the op
timization dimensions of AI algorithms for traditional pricing models, explores their value realization in practical application, concurrently
systematically identify and categorize risk derived from algorithmic application, and proposes targeted risk management strategies, aiming
to provide theoretical reference and practical guidelines for the in-depth implementation and standardized application of AI technology in
financial asset pricing.
Keywords
Artificial Intelligence (AI)Algorithms; Financial Asset Pricing; Model Optimization; Risk Assessment
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DOI: http://dx.doi.org/10.70711/aitr.v3i10.9217
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