AI Is Ready for a Bigger Role in Financial Analysis

The researchers also compared the results with those of a logistic regression model and an artificial neural network (ANN) that had been trained to predict the direction of earnings. Both of these models used 59 financial variables, such as the ratio of book value to price. The logistic regression model made predictions that were accurate about 53 percent of the time, while the accuracy rate for ANN was close to 60 percent, similar to GPT-4.

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The researchers suggest that GPT-4 likely used its understanding of economic reasoning to analyze the insights it formed from the financial ratios and trends it recognized in the statements. They employed a prompting technique known as chain-of-thought, which builds on intermediate reasoning steps to enable more complex analysis, to guide the LLM.

When the researchers didn’t use CoT prompting, GPT-4’s accuracy fell to about 52 percent. The LLM’s predictions were less likely to be accurate when it was evaluating a company that was small, had a higher leverage ratio, had recorded a loss, or had exhibited volatile earnings, the research finds.

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Kim, Muhn, and Nikolaev also evaluated whether GPT-4’s earnings forecasts could be put to economic use by informing a trading strategy. On the basis of information generated by GPT-4, they formed a long-short portfolio that delivered high risk-adjusted returns, relative to a benchmark, in backtesting (using historical data to validate a trading strategy).

Traditionally, LLMs have been considered tools to support an analyst’s work, but the researchers’ findings suggest that they have the potential to play a more central role in decision-making processes, rather than simply provide support. That’s not to say that LLMs should replace analysts. “Broadly, our analysis suggests that LLMs can take a more central place in decision-making,” the researchers write.

They note that humans still provide valuable insights that cannot be gleaned from financial statements. Professional analysts may bring a nuanced understanding of a company, the market, regulations, and more—but an LLM is stronger when it comes to predictions. The abilities of LLMs and analysts complement each other, the research concludes.