Under the wave of artificial intelligence (AI), AI is widely used in all walks of life, among which the combination of AI and quantitative strategy to empower equity investment has increasingly attracted the attention of top managers. GF Quantitative Multi-Factor applies AI to quantitative investment, data-driven decision-making, efficient and intelligent iteration, and strives to create actively managed quantitative products with long-term excess returns. According to the statistics of Galaxy**, as of December 15, the net value growth rate of GF Quantitative Multi-factor in the past 6 months was 197%, CSI 300 Index **1352%。
GF Quantitative Multi-Factor was established in March 2018, with **assets accounting for 0-95% of **assets, and is currently the ** managers Yi Wei and Li Yuxin. Through independent research and development of quantitative multi-factor model selection, the multi-factor model (including value, growth, fundamentals, market, other factors, etc.) is used to comprehensively evaluate the investment value and the risk-return characteristics of the portfolio, and select listed companies with high investment value to build a portfolio and pursue stable excess returns.
In the first half of this year, GF Quantitative Multi-Factor Investment Team introduced AI into its self-developed quantitative multi-factor model, gradually shifting from a traditional quantitative strategy dominated by mathematical statistics to an intelligent quantitative strategy led by machine-Xi based methods. After the strategic transformation, GF Quantitative Multi-Factor has entered the quantitative investment stage of "data-driven intelligent iteration", and continues to empower the whole investment chain with machine learning Xi technology.
Specifically, AI empowers and increases the efficiency of quantitative strategies in multiple dimensions: First, quantitative data sources can be greatly expanded, which can achieve diversification, multi-source, and heterogeneity, greatly expanding the breadth and depth of data, and better driving decision-making. Xi At the same time, the introduction of nonlinear methods can look at the law from multiple angles and improve the ability of the overall system. Third, data, algorithms, architectures, and technologies will continue to be updated and intelligently iterated. Based on low-correlation and diversified massive data, intelligently mine excess returns.
Combined with the ** regular report, since 2023, the A-share small-capitalization target has a relative advantage, GF Quantitative Multi-Factor has taken financial fundamental factors such as growth, profit, valuation, and dividends as the core, and superimposed trading factors such as momentum reversal and liquidity to continue to explore the application value of volume-price factors, and strive to achieve stable excess returns relative to the benchmark index.