Generative AI and the finance industry
Some good scenarios and some not-so-good scenarios.
Generative AI is going to transform a lot of industries in ways that we probably can’t fully anticipate right now. One of those industries might be finance.
AI has been used in finance for quite a while, of course. After all, AI is really about prediction, and that’s how you make money in the trading world. Renaissance Technologies, arguably the best quant trading firm in the world, has reportedly had some success using machine learning techniques, including natural language processing; two recent CEOs of the company, Robert Mercer and Peter Brown, were AI researchers. Other firms have followed suit, and there has generally been a lot of interest in the idea of using AI to trade. But as the WSJ’s Gregory Zuckerman recently reported, AI tools have been of surprisingly limited use so far:
One big problem [for AI in finance]: Investors rely on more limited data sets than those used to develop the ChatGPT chatbot and similar language-based AI efforts…[M]arket data is [also] “noisier” than language and other data…[And] unlike languages, markets can change quickly—companies alter strategies, new leaders make radical decisions and economic and political environments shift abruptly—making it harder to make trades using models reliant on historic, long-term data trends.
But if limited data is the problem, why not just use ChatGPT itself? It has more data than anyone. Sure, you can’t just ask an LLM what stocks to buy — they make way too many mistakes for that. But if there’s one thing ChatGPT is really good at, it’s analyzing the patterns in human language — patterns that probably don’t change that much from day to day.
I can see at least two pretty obvious ways to use that ability for investing:
Analyze what humans are talking about, in order to predict how they’re going to trade, and
Analyze human discussions to extract information about the fundamental value of companies and other assets.
Of course I don’t know yet how useful either of these approaches will be — no one does — but these applications wouldn’t obviously be subject to the limitations that Zuckerman mentions regarding traditional AI. So I expect finance people to put a lot of effort into investigating these two use cases.
Whether that will be good or bad for financial markets and the economy, of course, is another question entirely. It really depends on how much generative AI actually uncovers hidden information that can make markets allocate capital better, versus how much it just provokes a pointless zero-sum arms race and/or contributes to market instability. Here are a few scenarios.
ChatGPT versus r/wallstreetbets
Algorithms do a lot of financial trading these days, but humans still do fair amount (remember r/wallstreetbets and the saga of GameStop?). So why not use the LLM to do sentiment analysis — i.e., figure out what humans are talking about and thinking about, and then trade based on that? If people are excited and optimistic about a stock, you buy it before they do, and make money on the price appreciation; if they’re scared and pessimistic, you sell the stock. Pretty simple.
In fact, some people are already thinking about this. A new paper by Alejandro Lopez-Lira and Yuehua Tang uses ChatGPT to analyze news headlines and say whether the headline is good or bad for stock prices. They then trade based on that, and get results that look pretty impressive:
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