AI Can Write, But Is It Any Good at Picking Stocks?

Financial firms are racing to integrate artificial intelligence into as many of their operations as they can. But investors continue to struggle to harness the technology to the business’s central goal: predicting price movements in a way that boosts profits. It’s a challenge that’s proving far tougher than enlisting computer algorithms to summarize research reports. Even those sure that AI will one day revolutionize stock-picking think getting there will come through a long series of small tweaks and might initially produce a modest edge, though on Wall Street even a modest edge can mint billions.

In all sorts of roles, including customer service and making trade execution more efficient. JPMorgan Chase Inc. says that it sees more than 300 use cases for AI across its operations. In terms of boosting investment returns, hopes largely rest on machine learning, the subfield of AI where computers are trained on massive amounts of data to perform particular tasks. Machine learning encompasses both generative AI — the content-creating power behind ChatGPT — and predictive AI, which uses past results to forecast future outcomes. All of this builds on so-called quantitative, or quant, investing, a decades-old approach in which money managers have used computers to crunch piles of numbers to develop formulas for picking securities.