Source: Cao, Jiang, Wang & Yang (2024) — “From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses”
At first this sounds like a technical finance question. Then you look closer and realise it is also a question about attention, incentives, and whether people are using information or just being impressed by it.
The question of whether AI will replace financial analysts is usually framed as a binary. Either the machines take over or they don’t. This paper’s contribution is to reframe it as a question about comparative advantage — and the answer is more interesting than a simple yes or no. The researchers train an AI analyst on corporate disclosures, industry trends, and macroeconomic indicators, then evaluate its stock return predictions against those of human sell-side analysts. The result in the “Man vs Machine” contest: the AI surpasses most human analysts in predicting stock returns, particularly when information is voluminous and transparent — large amounts of structured data that human analysts struggle to process comprehensively. But humans maintain an edge in specific situations. When institutional knowledge is crucial — intangible asset valuation, firms in financial distress, situations where context and judgment matter — human analysts outperform the AI. The AI processes what’s in the data. Humans bring information that isn’t — relationships, industry intuition, the kind of qualitative judgment that comes from years of following a sector. The most important finding is the collaborative model. When human analyst forecasts and AI predictions are combined — “Man + Machine” — the result substantially outperforms either alone.
So I would not read this as a neat technology story. It is a messy information story. The tools improve, but people still have to decide what deserves attention. Annoying, but true.