[This article is an update of an article I wrote in 2017. I feel a bit prophetic now; If anything, the argument now is even stronger due to tools like ChatGPT – so I am reposting an updated version.]
A number of years ago I was at a technology CEO conference. At one point, there was a discussion about how some companies no longer want to go public. One seasoned CEO bemoaned the amount of time that goes into analyst meetings when you are a listed company. I pointed out that things might get better fast: the very technologies these CEOs are applying to other problems – neural networks, deep machine learning, and other tools of artificial intelligence – could completely eliminate traditional stock analysis and analysts. I believe even more than ever that there is no reason it can’t.
When it comes to things like deep machine learning, you may have heard the terms but not yet appreciated the results. But they are here. In the past few years, advances in synthetic vision have allowed driverless cars. (Every time your computer identifies you with facial recognition or a fingerprint it is using that technology.) Google reports processing one hundred million street addresses in an hour; they claim they processed all the addresses in France in that time. And today you can download free software to build your own artificial intelligence algorithms, in a few minutes, that can detect handwritten numbers with well over 95% accuracy; the code to do so requires only 47 lines of programming! Tools like ChatGPT require even less human input to function.
Investment Management is often a staid and conservative industry. Many believe investing is somehow immune to technological innovations. But technology has already changed so much in financial markets that they are barely recognizable from even ten years ago. Only a decade ago, large investment banks had trading floors that were the size of football fields – and stock, options and futures exchanges had bustling pits. Today, those venues and floors are nearly empty – or totally abandoned – and everything is handled via a computer algorithm.
So how does technology affect stock analysis? Wildly, I will argue.
Traditional stock analysts build large spreadsheets from financial data, trying to create a handcrafted forecast of the future results of a single company or business. It is all about being a mile deep and an inch wide: they spend enormous effort to try to get the right data about one company at a single point in time to decide if it will outperform or underperform business expectations.
But technology takes data analysis to another level. An algorithm like ChatGPT can analyze not just one company at one time, but rather can evaluate every piece of financial data ever reported for every company over all time to find patterns and identify opportunities – and do it all in minutes.
Is this for real? Note that it has been reported that ChatGPT has already designed active portfolios that have outperformed the market (and thus the majority of analysts) in legitimate back-testing. Amazing, but remember this as you cogitate on what AI can and can’t do: ChatGPT never forgets facts it has seen on the billions of web pages it has crawled. No person or team can do this through manual means.
What will be the end result? Note that algorithmic trading has made capturing trading profits harder, diminishing opportunities to capture alpha, and thus leading to the rise of index ETFs and factor investing. Instead of seeking fund outperformance, investors are focusing more on how to combine broad market and perhaps factor exposures using low-cost ETFs.
Some observers believe we will look back to this decade in 100 years and say the computational changes happening today are as fundamental as when mankind developed electricity, the transistor, the gas engine, or airplanes. Those technologies put the city lamp lighter, the blacksmith, and the Pony Express completely out of business. We can, and should, imagine this happening to stock analysis in the next few years.
The traditional stock analyst may be one of the next professions to become extinct.
Opinions expressed are current opinions as of the date appearing in this material only. While the data contained herein has been prepared from information that the author believes to be reliable, the author does not warrant the accuracy or completeness of such information. This communication is for informational purposes only. This is not intended as nor is it an offer, or solicitation of any offer to buy or sell any security, investment or product.