I think more and more people are starting to realize that AI is actually pretty good at picking stocks, or at least that's the promise. If you look at quantitative trading, it's one of the hottest fields in finance right now. The core idea is simple: use massive amounts of historical data to identify patterns and predict where markets are heading. That's how quant funds operate, and increasingly, it's what retail investors are trying to do with tools like ChatGPT and Gemini. But here's the thing. I've been thinking about this a lot, and I don't think it's as straightforward as people make it out to be.

The rise of AI-powered trading

Quantitative trading has been around for decades, but AI has supercharged it. Machine learning models, from support vector machines to deep learning neural networks, can now process enormous datasets, spot hidden correlations, and execute trades at speeds no human could match. According to the IMF, AI-driven ETFs already show significantly higher portfolio turnover compared to traditional actively managed funds, sometimes rotating holdings once a month versus less than once a year. The retail side is catching up fast. Bloomberg reported that retail investors, who own roughly 25% of the U.S. stock market directly and over 60% indirectly through retirement accounts, are now gaining access to tools that were once reserved for institutional players. Apps like Rallies are making this tangible by letting users deploy AI models to autonomously research and trade stocks. Rallies ran an interesting experiment: they gave multiple AI models virtual portfolios of $100,000 each and let them trade autonomously with live market data. In one competition, Grok 4 came out on top with a roughly 5.7% return and a 66.7% win rate, beating the S&P 500 over the test period. Gemini 3 Pro has also shown strong results, posting a 3.2% gain in a separate test window. In a longer eight-month simulation, Grok grew a $100,000 portfolio to approximately $156,000, a gain of about 56%, while GPT and Claude both finished around $127,000. Those numbers sound impressive. But I think they deserve a lot more scrutiny.

The psychology problem

Here's what I keep coming back to: the stock market is fundamentally built on human psychology. Prices don't just reflect earnings reports and balance sheets. They reflect fear, greed, herd mentality, overconfidence, and panic. Research from Cambridge University has shown that behavioral biases like loss aversion and herding behavior are key drivers of market volatility, often influencing price movements beyond what economic fundamentals would suggest. When you buy a stock, you're not just betting on whether a company will do well. You're betting on how millions of other people will react to information, to news, to rumors, to their own emotions. You're predicting collective human behavior, and that's an incredibly complex system. AI is excellent at finding patterns in historical data. But markets are forward-looking. A study highlighted on Reddit's r/science found that AI models often give misleading results when applied to stock market prediction because they're trained on past patterns that may not repeat. Morgan Stanley's behavioral finance research points out that even sophisticated investors fall prey to emotional decision-making, and those emotional swings are what actually move markets in the short term. AI doesn't feel fear. It doesn't experience the rush of a bull market. And that means it can miss the very forces that drive prices up or down.

The crowding problem

There's another issue that I think people overlook. If AI stock picking actually works, what happens when everyone uses it? Think about it. If I'm using ChatGPT to analyze stocks, and a few hundred thousand other people are doing the exact same thing with the same model, we're all going to get similar recommendations. We'll all buy the same stocks at the same time, drive the price up, and then the edge disappears. This is essentially what the Efficient Market Hypothesis predicts: when information is widely available and acted upon, it gets priced in almost instantly. Morningstar's research backs this up. Their analysis found that while AI and human analysts can complement each other, there isn't evidence to date that AI-powered funds outperform on a risk-adjusted basis. The reason? As AI makes forecasts more accurate across the board, markets become more efficient, which actually reduces the opportunity to generate outsized returns through stock selection. The edge isn't in having AI. The edge is in having AI that knows something others don't. And that's a much harder problem.

The insider information gap

This brings me to something else worth considering: insider knowledge. There's a whole layer of market-moving information that AI simply cannot access. Corporate executives making decisions behind closed doors, upcoming mergers being negotiated in private, regulatory changes being drafted in government offices. None of this shows up in the data that AI models are trained on. AI can only work with publicly available information. It can scrape news articles, parse SEC filings, and analyze earnings call transcripts. But the truly market-moving information, the stuff that creates the biggest price swings, often comes from places AI can't reach. Only people with the right connections and relationships have access to that kind of information (and trading on it is, of course, illegal). This is a fundamental ceiling on what AI can achieve in stock picking. It can optimize within the bounds of public knowledge, but it can't break through to the information asymmetries that drive some of the market's biggest moves.

So should you try it?

Honestly, I think it's worth experimenting with. The Rallies competitions show that AI models can make reasonable trading decisions, sometimes even beating the S&P 500 over short periods. Stanford research demonstrated that an AI analyst making stock picks based on 30 years of historical data significantly outperformed human investors in backtesting scenarios. But I'd approach it with eyes wide open. Here's what I'd suggest:

The bottom line

AI is a powerful tool for stock market analysis. It can process more data, faster, and with less emotional bias than any human. But the stock market isn't a pure data problem. It's a human behavior problem, and human behavior is messy, irrational, and deeply unpredictable. I think the people who will benefit most from AI in investing aren't the ones who blindly follow its picks. They're the ones who use it as one input among many, who understand its limitations, and who recognize that when everyone has the same sword, nobody has an advantage. The technology is new. The experiment is worth running. But don't bet the farm on it.

References