AI 'Outperforms' Human Financial Analysts, According to New Research

In the not-so-distant future, investors might be turning to robots for market advice. A new study has found that large language models — a type of artificial intelligence called LLMs — are better at conducting financial analysis than humans.

The findings, from researchers at the University of Chicago Booth School of Business, shed light on how AI can embrace machine learning to use companies' financial statements to predict revenue forecasts. According to the working paper, AI is able to produce a 60% rate of accuracy in predictive financial performance. Human experts’ accuracy tends to fall between 53% and 57%.

Researchers Alex Kim, Maximilian Muhn and Valeri V. Nikolaev determined that ChatGPT-4 "outperforms financial analysts in its ability to predict earnings changes [and] exhibits a relative advantage over human analysts in situations when the analysts tend to struggle."

This is a major development given how infrequently humans — including both retail and institutional investors — are able to beat the market.

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Will AI replace Wall Street analysts?

The University of Chicago study, which was published last week, comes on the back of other findings about AI's ability to achieve alpha, a measure of outperformance gauging the difference between what an asset returned and what its benchmark returned.

Companies like Danelfin and Boosted.ai are already utilizing the technology. Both feed AI bulk financial data and have it pick stocks, with the claim of being able to outperform the S&P 500 index in the short term.

In the University of Chicago study, researchers found that "the LLM generates useful narrative insights about a company's future performance," which means it can have an edge over a human analyst "due to its ability to quickly analyze large quantities of unstructured data and a vast knowledge base that enables [it] to model ... patterns, e.g., familiar business situations, in the data."

The study found that AI's score was "remarkably higher than that achieved by the [human] analysts," suggesting the technology could soon be part of the professional analysis process.

Concerns about AI replacing jobs span most industries, leading many experts to lean towards integrating machine learning in a way that supplements (not supplants) human labor. Nonetheless, a 2020 study conducted by S&P Dow Jones Indices compared actively managed funds to the performance of the S&P 500, finding that 89% of fund managers failed to beat the benchmark index. With firms now tapping AI in hopes of beating the S&P 500 — something the majority of fund managers fail to do — it may only be a matter of time until LLMs are knocking on analysts' doors.

Based on the results, the University of Chicago study suggests LLMs may take a central role in financial analysis decision-making. However, the authors clarify that the objective of AI in this particular case isn't to remove real people from the equation.

"Our findings indicate the potential for LLMs to democratize financial information processing," they wrote, adding that whether or not "AI can substantially improve human decision-making in financial markets in practice is still to be seen."

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