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On January 17, Yueran Technology unveiled its artificial intelligence trading system, "AI Trading," in Shanghai. Alongside the launch, the company announced the establishment of its first private equity fund based on the AI Trading System, called the Artificial Intelligence Trading Fund.
According to a report from the interface news, the Ziwu Yueran Artificial Intelligence No. 1 Securities Investment Fund is being recognized as "China's first artificial intelligence system to manage operating funds." This fund will invest in stocks, options, and commodity futures, and has already completed registration with the China Securities Investment Fund Association. It is set to go into trial operation by the end of this month, with a total scale of 30 million yuan.
Ye Chengyu, Chief Scientist at Yueran Technology, explained that the core algorithm of the trading system is built using an architecture inspired by the advanced artificial intelligence technology of AlphaGo. The system consists of two key components: a valuation network and a decision-making network. The valuation network predicts future market trends and probabilities, while the decision-making network uses deep reinforcement learning to determine optimal trade timing and order size, tailored to individual investor preferences. Like AlphaGo, the valuation network of the Love Trading system analyzes high-frequency data through convolutional neural networks without any human intervention.
At the press conference, Zhu Jiaqi, CEO of Yueran Technology, stated, "2018 will be the year of AI trading. This fund is just a test product. In the future, we plan to share our AI model with industry partners. Investment is a game for capital giants, with high barriers, high risks, and limited transparency. AI can offer us more diversified, customized, and personalized services."
Zhu Jiaqi, founder of Meridian Investment and Yueran Technology, brings over a decade of quantitative trading experience across U.S., European, and Indian markets, covering stocks, futures, and options. The team also includes Ye Chengzhen, a chief scientist specializing in deep learning, and CTO Zhu Xi, who has extensive experience in network mobile security within the U.S. financial sector.
Zhu Jiaqi is not afraid of the potential profits from quantitative trading. At the press conference, he recalled an incident five years ago known as the "Guangda Oolong Finger" case. He mentioned that due to errors made by Everbright Securities traders, there were significant transactions across over 100 stocks. However, he managed to seize a short-term trading opportunity through quantitative operations and made substantial gains that day, rewarding himself with an Aston Martin.
From quantitative trading to artificial intelligence, improved profitability and strong team support have driven Zhu Jiaqi’s team to transition from traditional methods to AI. According to him, integrating AI's self-learning capabilities into quantitative models helps adapt to different market conditions, uncover new stock selection factors, and better understand nonlinear relationships between these factors and stock returns. This enhances predictive accuracy and identifies previously unnoticed market opportunities.
However, some quantitative hedge funds argue that machine learning is just one of many statistical observation techniques. They believe that so-called AI is not particularly unique. The biggest challenge is that once a model proves effective and becomes popular, it tends to lose its edge quickly.
A recent example from the U.S. AI fund market highlights this issue. On October 18, 2017, EquBot LLC and ETF Managers Group launched the world's first AI-powered ETF, AIEQ. While it initially performed well, the fund struggled to match the market index. For instance, on October 25 and November 7, when the S&P 500 and Nasdaq Composite indices declined by 0.47%, 0.02%, 0.52%, and 0.27% respectively, AIEQ fell by 1.11% and 1.09%.
Despite advancements in model development and increased machine involvement in strategy creation, AI still faces two major challenges: First, achieving accurate predictions (overfitting) remains difficult in the short term. Second, the decision-making process of AI is often opaque, making it hard for ordinary investors to understand how the system operates. So far, no clear winners have emerged from AI’s deep involvement in investment.