Research on Optimization of High Frequency Trading Strategies and Market Impact Based on Artificial Intelligence
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v5i6.3355
Abstract
With the development of artificial intelligence technology, the high-frequency trading strategy has been significantly optimized in the financial market. This paper first reviews the basic concepts of high-frequency trading and its application status in financial markets, and deeply discusses the application of artificial intelligence (including machine learning, deep learning and reinforcement learning) in the optimization of high-frequency trading strategies, especially how to achieve higher returns and stronger market adaptability through algorithm improvement. Furthermore, this paper analyzes the impact of HFT on market microstructure and constructs a corresponding market impact model based on market impact and liquidity. Through empirical research, we verify the performance of different AI trading strategies in a variety of market environments, and reveal the effectiveness and potential risks of the strategies in real transactions. The research in this paper not only provides a new perspective for strategy optimization in HFT, but also provides theoretical support for future financial market participants to understand and manage the market impact.
Keywords
high-frequency trading; artificial intelligence; market influence
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[2] Risk identification and risk prevention and control in securities trading [J]. Jia Xiaowei. Economic Research Guide, 2023 (01).
[3] Capital market to accelerate the opening to the outside world Foreign investment actively seize the opportunities in China [J]. Wu Juanjuan; Ye Shijie. China Foreign Investment, 2022 (21).
[4] Preliminary research on the construction of the risk management system of securities and private equity fund companies [J]. He Chengh. China Sankei Industry, 2022 (16).
[5] The rule of law logic of inclusive and prudential regulation from the perspective of digital economy [J]. Liu Quan. Law Studies, 2022 (04).
[6] Programmatic and Htrading in European futures markets: regulation and new trends [J]. Sing Chen. China Securities and Futures, 2022 (01).
[7] High-frequency trading and price discovery in the stock index futures market [J]. Wei Lijian; David; Luo Xingguo; Zhang Yangfeng. Journal of Management Science, 2022 (01).
[8] Development and impact analysis of quantitative investment in China [J]. Zhang Xiaoyan; Zhang Yuan. Tsinghua Financial Review, 2022 (01).
[9] Research on the supervision of abnormal securities trading behavior [J]. Liu Peipei. Financial Development Research, 2021 (07).
[10] Institutional innovation of technical supervision of securities market in the Era of Fintech [J]. Lu Chenglong. Gansu Social Science, 2021 (04).
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