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A professor testing ChatGPT’s, DeepSeek’s and Grok’s stock-picking skills suggests stockbrokers should worry

Laila Maidan

19 min read

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Alejandro Lopez-Lira has been gauging the abilities of different artificial-intelligence models to picks stocks.

Alejandro Lopez-Lira has been gauging the abilities of different artificial-intelligence models to picks stocks. - MarketWatch photo illustration/iStockphoto, Alejandro Lopez Lira

Is artificial intelligence coming for the jobs of Wall Street traders? An assistant professor of finance at the University of Florida, Alejandro Lopez-Lira, has spent the past few years trying to answer that question.

Lopez-Lira has been experimenting with ChatGPT, DeepSeek and Grok to see if AI can be used to pick stocks. So far, he’s impressed with what the currently available AI chatbots can do when it comes to trading equities.

In an interview, Lopez-Lira acknowledged that AI is prone to making mistakes, but he has not seen the three versions he’s been using do anything “stupid.” His work comes as more market participants are thinking about the implications of AI for investing and trading.

“I don’t know what tasks out there analysts are doing with information that can’t be done with large language models,” Lopez-Lira said. “The only two exceptions are things that involve interacting in the physical world or having in-person conversations. But, other than that, I would imagine all of the tasks or most of the tasks can already be automated.”

Shortly after OpenAI Inc. released ChatGPT in 2022, Lopez-Lira began testing the chatbot’s skills. He wanted to know if ChatGPT, and AI in general, would show an ability to pick stocks. While there are numerous ways to approach that question, Lopez-Lira began with a simple exercise: Could the AI application accurately interpret whether a headline on a news story is good or bad for a stock? What he found surprised him.

Conducting a back test simulating historical stock-market returns, the study used more than 134,000 headlines from press releases and news articles for over 4,000 companies that were pulled from third-party data providers. The headlines were fed into ChatGPT using a programming language called Python. ChatGPT would then decide whether a headline was positive for a company, negative or unknown. The results were then saved in a data file and uploaded into statistical software in which headlines perceived as positive would result in a stock purchase. Negative headlines would trigger short sales, effectively betting against a stock in anticipation that it will fall in price. If ChatGPT was uncertain, no action was taken.