On April 2, the three major A-share indices opened lower and continued to decline. By the close, the Shanghai Composite Index fell by 0.74%, the Shenzhen Index dropped by 1.60%, and the ChiNext Index decreased by 2.31%. The total trading volume in the market was 1.86 trillion yuan, with over 4,300 stocks declining.
What will happen if you hand over 100,000 yuan to AI for stock trading?
Amid the craze for AI entities like &#34;Lobster,&#34; many investors are eager to try AI stock trading, including Wang Tao, who has five years of investment experience. Half a month ago, he entrusted 100,000 yuan to AI, attempting to test the returns of AI stock trading. Wang Tao believes that in the past, he was overly emotional: chasing highs, cutting losses, and frequent trading led to investment failures. Is rational AI more reliable than himself? As of now, Wang Tao's account has lost several thousand yuan, and he is increasingly skeptical about the uncertainty of AI stock trading.
On social media platforms, many high-return cases are becoming content to attract traffic. Some overseas bloggers claim that a &#34;Lobster&#34; turned a capital of 50 dollars into 2,980 dollars within 48 hours, achieving a return of 5,860%. Many domestic AI stock trading paid courses use such cases to promote the idea that AI is infallible and that investors can &#34;sit back and enjoy the profits,&#34; leading some investors to fall victim and suffer significant losses.
Is it feasible to completely entrust money to AI for management? Zhang Feng, vice president of the Shanghai Institute of Finance and Law, pointed out that any tool claiming &#34;AI can make you money&#34; is inherently worthy of high caution. Investment returns stem from risk pricing and market games; AI itself does not generate returns but optimizes information and efficiency. Promising &#34;to make money for clients&#34; is essentially misleading and may even involve illegal stock recommendations and fraud.
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AI Illustration/adan
Programmers worried about unemployment
Have started using AI for stock trading
Li Yanqi works as a programmer at an internet company in Beijing, focusing on front-end testing. In the past two years, the company has introduced an AI automated testing platform that can automatically generate test cases, identify visual deviations, and predict crash paths, often performing better than human programmers. Since the beginning of this year, he has noticed that AI is writing code faster and that many basic programming tasks have already been replaced by AI, leading the company to start reducing new hires. His role has shifted from executor to supervisor, and it is very likely to become a &#34;redundancy.&#34;
Li Yanqi began to contemplate his future path and wanted to try &#34;using AI to seize the era's dividends and start stock trading.&#34; In the past, Li Yanqi had also bought and sold stocks, and his approach was no different from that of most retail investors: &#34;After several years, I didn't make any money, and I even lost part of my principal.&#34;
Li Yanqi decided to change his approach, no longer manually monitoring the market, and began testing models to make decisions on his behalf. &#34;AI lacks human fears and greed, can be objective and rational, and is not easily swayed by emotions, which is a quality that humans lack in the stock market.&#34;
Li Yanqi leveraged his advantages as a programmer to write a program that collects daily market trading data, the latest company news, and changes in industry policies, organizing them into documents to send to the model.
Li Yanqi did not simply ask the model what stocks to buy today; instead, he set up several different identities for the model, simultaneously acting as a macro analyst, risk controller, and actual trading operator. The macro analyst is responsible for analyzing the overall market situation and pointing out which industries are receiving policy support; the risk controller assesses the potential probability of losses and reminds him when to reduce his investment; the trader provides a specific buying and selling plan based on the opinions of the first two roles.
Li Yanqi requires the model to write an investment diary after the market closes each day, recording the discussion process of these roles. He found that the model's investment philosophy differs from that of ordinary people; while ordinary people tend to chase stocks that have rapidly increased in price over a few days, the model analyzes the company's actual profitability and suggests buying stocks that are still at low prices and have growth potential. The model advises him to be patient in the face of price declines, stating that as long as the company's fundamentals remain unchanged, he should hold long-term and not be scared off by short-term market fluctuations.
In fact, on GitHub, open-source AI stock trading tools frequently trend, with some projects already receiving thousands of stars. The AI stock trading sector is beginning to enter the public's view. Xing Xing, chief economist of Jindonghui Enterprise Management Development (Beijing) Co., Ltd., told China News Weekly that individual investors in the A-share market account for a high proportion, with varying levels of professional ability, and a clear disadvantage in time and information, creating a strong demand for standardized, low-cost, and disciplined asset allocation services. Coupled with increased market volatility and a heightened awareness of long-term allocation, AI just fills the supply gap for inclusive wealth management.
Xing Xing summarized that the benefits of AI for investors mainly include: reducing information costs, quickly processing massive amounts of data, and reducing information asymmetry among retail investors; strengthening investment discipline by executing strategies through algorithms to overcome human weaknesses like chasing highs and selling lows; improving allocation efficiency to achieve personalized asset combinations and dynamic rebalancing, suitable for long-term holding by ordinary investors. At the same time, it continuously empowers risk alerts, position monitoring, and investment education support, making professional financial services more accessible.
Reliance on large models may lead to greater losses
Li Yanqi felt that the AI model was like a tireless assistant, capable of discovering information he usually overlooked. To verify the effectiveness of this method, Li Yanqi took out 20,000 yuan and placed it in a separate account for real trading testing. He followed the model's suggestions for buying and selling without trusting his intuition, and after a few months, he found that the fluctuations in his account funds had become very small, with no occurrences of losing a lot of money in a single day as before.
He now has a bigger plan to write this method of constructing prompts into an automatically operating system, allowing the program to collect data, analyze it, and place orders on its own, achieving automated trading without human intervention.
However, true investors have a different understanding of AI stock trading. Xie Minghui, a full-time teacher at the School of Economics and Management at Wuchang University of Technology and a trader with over 20 years of investment experience, has a professional academic background in finance that continuously shapes his understanding of real trading operations, achieving a return rate as high as 200% last year
Xie Minghui has also started using AI in his daily trading. He believes that the biggest role of AI currently is to process textual information. In the past, when researching a listed company, Xie Minghui needed to download hundreds of pages of annual financial reports from various websites, searching for key data page by page, as well as looking for statements from company executives and industry changes, which took a lot of time. Now, after handing over this information to AI, the model can help him extract key data such as the company's operating income, profit changes, and core product sales in a very short time, organizing it into a table, which greatly saves time.
Xie Minghui realizes that there are many people around him who lack basic understanding. Upon hearing that AI can trade stocks, they thoughtlessly follow the trend and use it. &#34;These people do not understand what the price-to-earnings ratio is, do not understand what value investing is, and only know to let AI provide a stock code, then invest all their money.&#34; Xie Minghui believes this approach carries significant risks.
Taking corporate governance as an example, Xie Minghui found that some listed companies have controlling shareholders who hold a large proportion of the stock. When the stock price is driven up, these major shareholders choose to sell their stocks to cash out. &#34;This change in human nature and calculation of interests is something that AI cannot predict through a few financial statements. Retail investors only look at the technical chart analysis provided by AI, without understanding the distribution of interests behind the company, making it easy to get trapped.&#34;
Currently, many tools referred to as intelligent investment advisors have emerged in the market, providing users with various data analysis services. Xie Minghui believes these tools do have some advantages, as they can break the information gap and recommend different proportions of stock and bond combinations based on users' risk tolerance. However, these tools also have obvious disadvantages; the results they generate rely entirely on past data and do not know what unexpected events will occur in the future.
&#34;Many financial platforms have launched related services, and some platforms exaggerate returns while downplaying risks in their marketing, which can easily lead to short-term cognitive biases among investors.&#34; Xing Xing also realizes that AI models over-extract random patterns from historical data, treating noise as signals, especially optimizing for extreme returns like limit-up stocks. The more impressive the historical performance, the more fragile the real performance becomes. Thus, a natural data bias is formed.
He believes: &#34;For investors, this may lead to situations where AI cannot adapt in real-time to regulatory changes, capital behavior, and emotional fluctuations. This means that the investment advice given by AI often only exists in an ideal environment and cannot be replicated in the real market.&#34;
A New Gimmick to Harvest Investors
Some criminals have begun to exploit these &#34;high-end&#34; terms to create scams, targeting many investors who do not understand technology. On the Black Cat Complaints platform, there have been reports of rights protection information regarding AI stock trading software, with most victims being middle-aged and elderly individuals.
A netizen reported on the Black Cat Complaints platform that an elderly family member had some savings and needed to preserve and increase their value. A software company in Hebei named Yuanda took advantage of this psychology and developed a quantitative stock trading software that claims to use the latest models to calculate buying and selling points, named &#34;Yuyue Longmen.&#34; Sales personnel, while promoting it, showed the elderly a picture of profits, which indicated that this software had achieved a return rate of 1000% since its operation began in September 2024
Many elderly people have spent a high price to purchase a lifetime license for this software, and then followed the software's prompts to buy and sell stocks, resulting in no profits and a loss of more than half of their principal. This netizen discovered that the &#34;tenfold&#34; return data displayed by the software was artificially modified in the background, with the purpose of attracting investors to pay.
Even more exaggerated are several &#34;fake&#34; software companies represented by Wuhan Baiyu Quantitative and Shenzhen Yongjie Quantitative. According to an investigation by the 21st Century Business Herald, these companies superficially promote &#34;AI quantitative stock trading&#34; as a marketing gimmick, but in reality, they have built a scam loop of &#34;pyramid-style recruitment + Ponzi funding.&#34; Under the banner of &#34;AI quantitative trading&#34; and &#34;intelligent follow-up investment,&#34; they claim to use self-developed AI systems for automatic stock trading, with monthly returns reaching 150%. In fact, the funds never entered the securities account but flowed into private accounts through third-party payment channels, and the profits seen by investors are just modified numbers in the background (virtual accounts).
The number of victims in these cases has reached hundreds, with the total amount defrauded accumulating to tens of millions. Recently, many similar companies have faced explosive failures, and the police have initiated investigations into some of the involved cases.
Yang Jianjun, a professor at Northwest University of Political Science and Law, told China News Weekly that currently, there are three typical &#34;AI guaranteed profit&#34; scams, concentrated in AI training courses, AI stock trading and investment software, and AI automatic sales and content creation fields. These gimmicks often easily induce payments and accurately attract traffic, and multiple fraud cases have already emerged in practice.
Yang Jianjun reminds investors that any project promising &#34;low threshold, high returns, and guaranteed profits&#34; fits the basic characteristics of fraud. For software or services related to investment, investors must check through official websites such as the China Securities Regulatory Commission to see if the relevant institutions have legal qualifications. &#34;AI is a powerful efficiency tool that can help us analyze information and generate content, but it is not a 'magic hand' that can guarantee business success or investment returns. Do not let your guard down just because it is wrapped in 'AI.'&#34;
A scholar in the financial field pointed out that currently, the rapid development of technology has indeed outpaced regulation. On one hand, as investors, they need to treat AI stock trading with caution; on the other hand, government departments and regulatory agencies should accelerate the formulation of relevant laws and regulations to avoid systemic risks.
Yang Jianjun stated that there is currently intense discussion in the industry about whether general large models (such as DeepSeek) that directly recommend stocks should be subject to securities investment advisory license regulation. With the emergence of investment losses caused by &#34;AI illusions,&#34; it is expected that within the next 2-3 years, mandatory regulations on the disclosure of AI investment advisory information will be implemented.
Previously, the State Administration for Market Regulation and the National Standardization Administration issued the &#34;Cybersecurity Technology Safety Specifications for Data Annotation of Generative Artificial Intelligence.&#34; This document stipulates the safety requirements for data annotation platforms or tools for generative artificial intelligence training, safety requirements for data annotation rules, requirements for data annotation personnel, verification requirements for data annotation, and describes methods for evaluating data annotation safety
Zhang Feng suggested from the perspective of legislative proposals to quickly incorporate the four labeling requirements into the revision of the &#34;Administrative Measures for Securities Investment Advisory Business&#34; or special financial AI regulatory regulations, to enforce mandatory implementation and strict law enforcement, promoting the compliant, transparent, and responsible development of AI in the financial sector.
(In the text, Wang Tao is a pseudonym)
Published on April 6, 2026, Issue No. 1230 of &#34;China News Weekly&#34; magazine
Magazine title: The Chaos of AI Stock Trading
Reporter: Meng Qian
Editor: Min Jie

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On April 2nd, the three major A-share indices opened lower and continued to decline, with the Shanghai Composite Index falling by 0.74%, the Shenzhen Index dropping by 1.60%, and the ChiNext decreasing by 2.31%. Wang Tao entrusted 100,000 yuan to AI for stock trading, resulting in a loss of several thousand yuan, raising doubts about the reliability of AI. High-yield cases on social media attract investors, but many have been deceived as a result. Expert Zhang Feng warns that any tool claiming "AI can make money for you" should be approached with caution, as investment returns stem from risk pricing and market competition, and AI does not generate profits

The "sure-win" scam has been exposed