Seizing the Moment: A Study on the Practical Efficacy and Economic Value of AI Prediction Algorithms in High-Frequency Trading

Ying Li

doi:10.6981/FEM.202602_7(2).0010

Authors

Ying Li

DOI:

https://doi.org/10.6981/FEM.202602_7(2).0010

Keywords:

High-Frequency Trading; AI Prediction Algorithm; Technical Efficacy; Economic Value; Market Impact.

Abstract

This study focuses on the application effects and economic benefits of artificial intelligence (AI) prediction algorithms in the field of high-frequency trading (HFT). It establishes a comprehensive evaluation framework covering "technical characteristics - economic returns - market responses" to fully assess the overall performance of AI models in real HFT scenarios. Quantitative methods are used to analyze the key indicators of the algorithms, and real-world data is employed to verify their feasibility. Through an in-depth exploration of the interactive impacts among AI technology, trading returns, cost structure, and market liquidity, the study reveals that AI prediction algorithms not only significantly improve the operational efficiency of HFT but also enhance its profitability notably, while also driving changes in the improvement of market-wide liquidity. This research provides theoretical guidance and practical operation guidelines for financial institutions in formulating strategic plans and for regulatory authorities in developing policies.

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