It's all about tilting probabilities in your favor by making connections and the less obvious the connections that the computer can come up with the more valuable they are.*
From the University of Chicago, Booth School of Business, Chicago Booth Review, September 3:
Ten ways investors are, or should be, using large language models
Great investors tend to be avid readers, always hunting for some piece of information to give them a financial edge. There are decades if not centuries of examples of pros who have combined something they’ve read—in a book, article, or regulatory filing—with their market experience to gain a lucrative insight. For one example, investment manager Jim Chanos’s careful reading of Enron’s regulatory filings, and his past experience with fraud detection, led him to suspect accounting irregularities at the company. He made $500 million when Enron filed for bankruptcy in 2001.
These days, though, even the most avid readers would have trouble competing with the volume of financial insights that artificial intelligence, in the form of large language models, can uncover. LLMs have gained mainstream popularity thanks to OpenAI’s ChatGPT, an advanced chatbot powered by a series of generative pretrained transformer language models. OpenAI has released several versions of its LLM, with GPT-3.5, GPT-4, and GPT-4o among the most recent.
Almost a decade ago, Chicago Booth Review published a feature titled “Why words are the new numbers” about a coming revolution in text analysis. That predicted revolution arrived, and it demolished the monopoly that numbers long held in forecasting models. Numbers are still important, of course—but text analysis is ascendant and everything is now potential data.
The candid speech during earnings calls? Data. The formal prose of annual filings? Data. News articles? Data. The entire internet? Data.
LLMs are trained on vast amounts of text covering a broad range of information and can apply their repositories of knowledge to evaluate new information. Where a human will depend on past experience and intuition, LLMs use data and patterns from their training.
And they operate at a scale that exceeds human capabilities, quickly analyzing mountains of text and allowing traders and investors to mine insights faster and more accurately than was ever possible. They can connect ideas from different parts of a text to create a better understanding of its overall content. LLMs can even be customized, trained to become experts on accounting irregularities—or, say, mall leases or risk management.
Every asset manager with a technology team now has the opportunity to wield—and profit from—an enormous knowledge base, and many are doing just this. Funds are using LLMs to read and glean insights from earnings call transcripts, 10-K regulatory filings, annual reports, social media, and streaming news headlines—searching for clues about a company’s direction.
From the output of this text mining, LLMs can create direct trading signals (instructions to buy or sell) or develop new predictive variables for their forecasting models. If you hold actively managed funds in your retirement accounts, there’s a good chance the pros running the strategies are harnessing the research power of LLMs.
It makes sense to ask whether the advantages of LLM strategies will disappear as soon as everyone else uses them too. That’s been the outcome with arbitrage strategies—their returns fall when too many investors are chasing the limited opportunities. However, the opportunities here appear more bountiful than in arbitrage scenarios. With the field in its early stages, researchers are still finding new ways to apply AI to tease out investment insights and trading opportunities. Plus, new data sources that run the gamut from text to image, audio, and video are enabling the uncovering of information that is not so easily priced into the markets.
Researchers, like traders, are scrambling to stay ahead of the curve. Here are 10 of their recent observations....
Or:Over the years we've mentioned one of the oddest phenomena in science, the simultaneous discovery or invention of something or other, the discovery/invention of the calculus by Newton and Leibniz is one famous example (although both may actually have themselves been preceded) but there are dozens if not hundreds of cases. Here's a related phenomena.
On Saturday September 23, 6:28 AM PDT we posted "Cracking Open the Black Box of Deep Learning" with this introduction:
One of the spookiest features of black box artificial intelligence is that, when it is working correctly, the AI is making connections and casting probabilities that are difficult-to-impossible for human beings to intuit.Today Bloomberg View's Matt Levine commends to our attention a story about one of the world's biggest hedge funds and prize-putter-upper of what's probably the most prestigious honor in literature, short of the Nobel, the Man Booker Award....
Try explaining that to your outside investors.
You start to sound, to their ears anyway, like a loony who is saying "Etaoin shrdlu, give me your money, gizzlefab, blythfornik, trust me."
See also the famous Gary Larson cartoons on how various animals hear and comprehend:...
AI, Training Data, and Output (plus a dominatrix)
Maybe:
"The Dark Secret at the Heart of AI"
And many, many more. The 'search blog' box upper left can come in handy.
