Active investors have long attempted to get an informational edge on markets by using artificial intelligence (AI) processes to retrieve and process data. For example, tools that gauge sentiment from social media or scrape text from company financial reports predate ChatGPT by many years.
Material information gleaned from running AI processes is very likely a subset of the vast information set known by the market in aggregate and reflected in market prices. If new information is obtained, the process of acting on that information incorporates it into market prices.
Another reason to question AI’s role in helping with market timing is limitations with its predictions. AI’s forecasting ability fares well when assessing patterns that are relatively stable. The market is fantastically complex. So much so that no one knows exactly how much a particular piece of information impacts a price, because there are so many other simultaneous inputs. AI trying to predict market prices is like self-piloting cars trying to read stop signs with words, shapes, and colors that differ every day.
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