There’s been a lot of excitement and interest in artificial intelligence (AI) this year, largely thanks to the introduction of ChatGPT and other generative AI tools. It seems every day we hear about new potential applications of AI and how they can change the business world forever. Against that backdrop, it’s useful for investment professionals to ask themselves how AI may change their role going forward.
The reality is that the current generative AI tools being discussed are not likely to be as useful for investing as many would hope. While ChatGPT is often portrayed as a person-like intelligence, the software is really just mimicking the ideas and language of people. It can combine words into novel sentences and present concepts, but it does not “understand” anything. It is a long way from being able to truly “think.”
Of course, that does not mean AI is not useful; in principle, ChatGPT and generative AI can review large amounts of data, including financial data on stocks, and then make recommendations to investors. It can also construct a portfolio of stocks based on an idea such as mean-variance (M-V) optimization, and in theory, it could even come up with a novel approach to building a portfolio based on some mathematical rule similar to M-V optimization.
All of this is largely the realm of the future. At present, ChatGPT and other generative AI tools often make mathematical errors in calculations, they sometimes use the wrong numbers in calculations (again because they don’t “understand” what the numbers are), and they can draw faulty conclusions from data, especially when dealing with unique scenarios.
So for now, investment professionals should not consider turning to ChatGPT to construct a portfolio. However, ChatGPT and other AI tools are effective at language, since they are large language models (LLMs), so they can effectively create presentations, write basic research reports or at least sections of them, craft emails to clients and perform similar tasks. Moreover, in the near future, it’s very likely that we will see generative AI being used to deal with clients through web browsers (think advanced chatbots), help with regulatory filings and assist in client onboarding and account issues. These types of tasks require specialized programming for software tools, but they are within the capabilities of a generative AI system.
Beyond this though, AI does have a role in portfolio construction. That role is in helping people identify and capitalize on interesting opportunities. AI can comb through large amounts of numeric data to do things like identify a list of companies with low P/E ratios, which is a simple enough task in principle, but often made more difficult by special item adjustments. More importantly, AI can identify variables linked to historical return outperformance and then find investment possibilities based on that, or it could forecast real-time expected returns for various stocks based on models like the Fama and French 4 Factor Model. Of course, none of this is truly new; hedge funds have been using computers for decades to perform similar quantitative analysis and trading. Firms like Renaissance Technologies and Two Sigma are built on it. In that sense then, the AI revolution for investment professionals is not coming—it’s already here.
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