Jesse Livermore scanned trendlines. Warren Buffett sought a margin of safety. Peter Lynch bet on growth rates. In the long history of markets, trading systems and investment formulas hold an honored place.
Yet even the finance legends couldn’t have predicted what artificial intelligence proponents are dreaming up these days, thanks to the computational firepower of language models like ChatGPT.
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In this new world, there are automated programs that blare out warnings if corporate executives go on unnecessary tangents or jump between subjects — potential signs of anxiety about the future. Another AI model dissects product blueprints and graphs from business slides in an attempt to forecast stock swings. There’s even an trading tool that compares actual statements from the C-suite with imaginary dialogue cooked up by machines to figure out — somehow — market liquidity.
The list of ingenious-sounding investing ideas churned out by academia is growing by the day.
Of course, quants have spent decades trying to uncover hidden stock omens across Corporate America with mixed success. But natural language processing — the branch of AI that deals with text comprehension — is the hottest toy again thanks to the wonders of chatbots. The rush to cash in is leveraging long-standing ties between university researchers and systematic investors — and opening a new frontier in so-called sentiment analysis.
“Basic sentiment analysis based on dictionary — absolutely, that is being arbitraged away,” said Mike Chen, the head of alternative alpha research at Robeco, which runs $82 billion in quant strategies. “There’s so much more you can do.”
At its core, linguistic data-crunching seeks to help quants get better at predicting the future by analyzing the meaning of the text behind the numbers. Think of an analyst reading the news or listening to earnings calls, but scaled up across a myriad number of sources tracking every single company at every single moment — in the blink of an eye.
Beyond text analysis, there’s a plethora of research dissecting speaking tones, facial expressions in videos and even emojis. At Robeco, quants recently started analyzing the tone and pitch of executives on earnings calls for a sign of their true confidence. Stockpulse, a data provider, this year began compiling what influencers are saying about the economy and various companies on TikTok.
Over at AllianceBernstein, data scientists Andrew Chin and Yuyu Fan are going all-in on AI tools to find hidden meanings in corporate spiel. Not every attempt has worked. For instance, when they dug into how Chinese companies summarize on-site visits from brokers, they found that the more complex the text — like the length of sentences and unnecessary words — the more evidence that the firm in question is struggling.
On the other hand, the number of words divided by the length of the broker event, a proxy for speaking speed, didn’t mean much. In US earnings calls, they studied the use of the “we” pronoun as a sign of collaboration and unity. That also proved meaningless.
“We really try to generate a broad set of signals — sometimes hundreds — but it doesn’t mean all of them will work,” said Fan, a senior data scientist at the $669 billion manager.
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Earnings-call sentiment has been turning more positive over the years, a potential sign executives are gaming the signal. Source: Wolfe Research
Article by Justina Lee of Bloomberg News, Advisor Perspectives
