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In a bold experiment that captured the attention of Reddit and financial enthusiasts alike, a Medium author from the *Coding Nexus* publication handed over $100 to OpenAI’s ChatGPT (GPT-4o model) to manage a stock trading portfolio for two months, starting in July 2025. The goal? To see if an AI language model, with no formal financial training, could beat the market by trading U.S. micro-cap stocks—small companies valued under $300 million, often ignored by Wall Street analysts. The results, detailed in a viral Medium article and inspired by a Reddit post from user u/OpenArcher7341, were surprising: a 29% return in just eight weeks, outpacing major benchmarks like the S&P 500 and the biotech-focused XBI index. But while the experiment showcased AI’s potential in retail investing, it also raised questions about risk, scalability, and the sustainability of such gains.
The Setup: ChatGPT as a Stock Picker
The experiment was designed to test ChatGPT’s ability to analyze publicly available data—news, earnings reports, and market trends—to make trading decisions. The rules were straightforward:
- Starting Capital : $100, invested only in whole shares to mimic a beginner’s budget.
- Stock Focus : U.S. micro-cap stocks, particularly in volatile sectors like biotech, where AI might uncover undervalued opportunities.
- Trading Frequency : Weekly trades based on ChatGPT’s recommendations, prompted with real-time data from Yahoo Finance via Python scripts.
- Risk Management : Stop-loss orders capped losses at 10-15% per stock, and no complex strategies like short-selling or options were allowed.
- Human Role : The author manually executed trades on a low-fee platform like Robinhood, as ChatGPT can’t directly access brokerage accounts.
The portfolio’s performance was benchmarked against the S&P 500 (broad market), Russell 2000 (small-cap index), and XBI (biotech small-cap index). The experiment began in a bullish market phase in July 2025, with the author sharing code and methodology on GitHub for transparency.
Month One: A Stellar Start
The first month, aligned with the Reddit experiment that sparked widespread interest, was a breakout success. ChatGPT leaned heavily into biotech micro-caps, identifying companies with positive catalysts like clinical trial results or undervalued earnings. By the end of July, the $100 portfolio grew to $125—a 25% gain. This handily beat the S&P 500 (+3%), Russell 2000 (+5-7%), and XBI (+10-12%) over the same period.
Key to this success was ChatGPT’s ability to process vast datasets quickly, spotting short-term opportunities that human analysts might overlook in the under-covered micro-cap space. For example, it recommended stocks with upcoming FDA approvals, capitalizing on price spikes. The stop-loss rule proved effective, with one trade limited to a 12% loss after bad news hit. Risk metrics, calculated via Python, were impressive: a Sharpe Ratio of 0.94 (indicating strong risk-adjusted returns) and a Sortino Ratio of 2.00 (showing minimal downside volatility). Posts on X and articles from outlets like Futurism praised the results but cautioned that one month was too short to draw broad conclusions.
Month Two: Steady Gains Amid Volatility
The second month, detailed in the Medium article, showed continued growth but at a slower pace. ChatGPT diversified slightly into tech micro-caps while maintaining its biotech focus, selling winners from July and reallocating based on fresh prompts. Despite a market dip, the portfolio grew from $125 to $129 by late August, yielding a cumulative 29% return on the initial $100. This outperformed the S&P 500 (+4% over two months), Russell 2000 (+8-10%), and XBI (+15-18%).
Challenges emerged, however. One stock plummeted 18% after negative news, but the stop-loss limited the portfolio’s hit to 2%. Transaction fees, though minimal with a $100 budget, shaved off about 1% due to weekly trading. ChatGPT also made occasional questionable recommendations, like over-concentrating in a single sector, which the author noted but didn’t override to preserve the experiment’s integrity. The final $29 profit—while modest in absolute terms—was a proof-of-concept for AI-driven trading on a small scale.
What It Means: AI’s Promise and Pitfalls
The experiment highlights AI’s potential to democratize investing for retail traders with limited resources. ChatGPT’s ability to sift through news, financials, and trends gave it an edge in the niche micro-cap market, where human analysts are scarce. As the author noted, this aligns with academic studies, like one in *Finance Research Letters*, showing LLMs can generate profitable trading signals in simulations.
However, the results come with significant caveats:
- High Volatility : Biotech micro-caps are prone to 20% daily swings, making the strategy risky. A single bad month could erase gains.
- Scalability Issues : While $100 trades face minimal liquidity constraints, scaling to larger sums (e.g., $10,000) could falter in illiquid micro-cap markets, as noted by University of Florida professor Alejandro Lopez-Lira in Morningstar.
- Market Dynamics : If AI trading becomes widespread, it could eliminate the very inefficiencies ChatGPT exploited, a point Lopez-Lira emphasized.
- Human Oversight Required : ChatGPT needed curated prompts and manual trade execution, meaning it’s not a fully autonomous solution.
- Short-Term Luck? : Two months is a small sample. The Reddit user plans a six-month follow-up to test consistency, while earlier 2023 experiments showed ChatGPT favoring safer ETFs over risky picks.
Broader Implications and Next Steps
This experiment underscores AI’s growing role in personal finance, from budgeting apps to stock picking. However, experts urge caution. Regulatory bodies like the SEC are scrutinizing AI-driven financial advice, and ChatGPT isn’t a licensed advisor. For those inspired to try this, the author recommends starting with a simulator like Investopedia to avoid real losses. The Medium article provides sample prompts and code for replication, while the original Reddit post in r/dataisbeautiful offers raw data and visualizations.