Application and practice of AI technology in quantitative investment

ArXiv ID: 2404.18184 “View on arXiv”

Authors: Unknown

Abstract

With the continuous development of artificial intelligence technology, using machine learning technology to predict market trends may no longer be out of reach. In recent years, artificial intelligence has become a research hotspot in the academic circle,and it has been widely used in image recognition, natural language processing and other fields, and also has a huge impact on the field of quantitative investment. As an investment method to obtain stable returns through data analysis, model construction and program trading, quantitative investment is deeply loved by financial institutions and investors. At the same time, as an important application field of quantitative investment, the quantitative investment strategy based on artificial intelligence technology arises at the historic moment.How to apply artificial intelligence to quantitative investment, so as to better achieve profit and risk control, has also become the focus and difficulty of the research. From a global perspective, inflation in the US and the Federal Reserve are the concerns of investors, which to some extent affects the direction of global assets, including the Chinese stock market. This paper studies the application of AI technology, quantitative investment, and AI technology in quantitative investment, aiming to provide investors with auxiliary decision-making, reduce the difficulty of investment analysis, and help them to obtain higher returns.

Keywords: Artificial Intelligence, Quantitative Investment, Market Trend Prediction, Model Construction, Program Trading, Equities

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 1.0/10
  • Quadrant: Philosophers
  • Why: The paper is primarily a high-level overview and literature review of AI and quantitative investing, lacking advanced mathematical proofs or derivations. It also contains no data, backtests, or implementation details, serving more as a conceptual introduction than an empirical study.
  flowchart TD
    A["Research Goal<br>How to apply AI to Quantitative Investment<br>for profit & risk control?"] --> B["Methodology<br>Collect & Analyze Financial Data"]
    B --> C["Input<br>US Inflation, Fed Policy, <br>Equity Market Trends"]
    C --> D["Computational Process<br>AI Model Construction<br>Machine Learning & Program Trading"]
    D --> E["Key Outcomes<br>Improved Trend Prediction<br>Better Risk Control & Returns"]