One thing AI might struggle with is picking stocks. Traditional active investors have attempted to get an informational edge on markets by using AI processes to retrieve and process data for years.
While these efforts may have been aimed at selecting stocks that would outperform markets, it is not clear AI tools are a recipe for consistently generating abnormal returns.
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 (buying or selling stocks and bonds) incorporates it into market prices. As more investors employ these tools, any edge from doing so should wane.
AI’s forecasting ability fares well when assessing patterns that are relatively stable. For example, my phone’s navigation app is often successful at guessing when I am commuting to work because I come to the office on the same days each week. Self-driving cars stop at a stop sign because these visual cues are unchanging.
AI is far less likely to successfully predict changes within complex systems that are as dynamic as stock and bond markets.
AI trying to predict market prices is like self-driving cars trying to read stop signs with words, shapes, and colours that differ from one day to the next. The continuous emergence of new information material to market prices is antithetical to static patterns fostering predictability.
Eventually, we may reach a point where, like the internet, it is hard to fathom a time before broad AI usage.
That means investors do not need a narrow sector fund or concentration in a handful of stocks to capture AI-fuelled gains.
A broadly diversified portfolio is likely to capture what many view as a sea change event in progress.
Wes Crill is a senior investment director and vice-president at Dimensional Fund Advisors
