Does Investment Research Make Sense in the Age of AI?

AI is transforming investment research worldwide by automating predictions and analysis while highlighting the enduring need for human intuition.

Large language models can outperform analysts in some areas but human intuition is invaluable.

The late Byron Wien, a prominent markets strategist of the 1990s, defined the best research as a non-consensus recommendation that turned out to be right. Could AI pass Wien’s test of worthwhile research and make the analyst job redundant? Or at the very least increase the probability of a recommendation to be right more than 50 per cent of the time? Well, it is important to understand that most analyst reports are devoted to the interpretation of financial statements and news. This is about facilitating the job of investors. Here, modern large language models simplify or displace this analyst function.

Next, a good amount of effort is spent predicting earnings. Given that most of the time profits tend to follow a pattern, as good years follow good years and vice versa, it is logical that a rules-based engine would work. And because the models do not need to “be heard” by standing out from the crowd with outlandish projections, their lower bias and noise can outperform most analysts’ estimates in periods where there is limited uncertainty. Academics wrote about this decades ago, but the practice did not take off in mainstream research. To scale, it required a good dose of statistics or building a neural network. Rarely in the skillset of an analyst.

Change is under way. Academics from University of Chicago trained large language models to estimate variance of earnings. These outperformed median estimates when compared with those of analysts. The results are fascinating because LLMs generate insights by understanding the narrative of the earnings release, as they do not have what we may call numerical reasoning — the edge of a narrowly trained algorithm.[…]

AI is transforming investment research worldwide by automating predictions and analysis while highlighting the enduring need for human intuition.

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