We’ve all heard about the impact artificial intelligence (AI) is making on our lives by writing copy, making entire movies, creating “deepfakes” and driving cars. It’s also being used for financial decision-making, and some suggest it will soon take over from “human” investment advisors. We have our doubts.
Excitement regarding AI is commonly cited as the reason for the impressive performance of U.S. stocks this year; as of mid-September, the S&P 500 Index had yielded a 17% Canadian dollar total return, including dividends. Yet, ironically, funds managed using AI have struggled to keep pace with the benchmark.
According to a recent story in The Wall Street Journal, “at least 13 exchange-traded funds have put artificial-intelligence applications in charge of managing their portfolios. Almost all have missed out on this year’s tech-led market rally and are lagging behind benchmark indexes such as the S&P 500 – an irony given the investor enthusiasm regarding AI subjects and likely a sign of the technology’s limitations in the ultracompetitive world of stock picking.”
The AI Powered Equity ETF, ticker symbol AIEQ, is the oldest exchange-traded fund the article describes. It was launched in 2017, and it runs on IBM’s Watson supercomputer, which was famously used in 2011 to compete on, and win, the TV quiz show Jeopardy! The fund bases its bets on an analysis of millions of news articles, social media posts, analyst reports and financial statements. This year, it is up 9% – roughly half as much as the broader market. This is because it spread its bullish bets across too many stocks and missed out on the surge in the Magnificent Seven – Nvidia, Apple, Microsoft, Alphabet, Meta, Amazon and Tesla – which benefited from investors’ belief that these massive tech companies will benefit disproportionately from AI.
AIEQ tracked the performance of the benchmark in its early years, and even outperformed at times. Then in 2022, it got clobbered by the rout triggered by the U.S. Federal Reserve’s interest rate hikes. Since inception, it has produced a total Canadian dollar return of 51%, less than half of the 109% achieved by the S&P 500 Index.
The reality is humans have been trying to use computers and AI to beat the market for decades, sometimes triumphantly and sometimes disastrously.
Jim Simons’ use of AI for investing is one of the success stories, which was chronicled by Gregory Zuckerman in a 2019 New York Times Best Seller titled The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. The author describes the book as the untold story of how a secretive mathematician pioneered algorithmic trading (the basis for so-called “quant funds”) and became the greatest moneymaker of all time. The inside cover asserts, “No other investor – Warren Buffett, George Soros, Peter Lynch, Steve Cohen, or Ray Dalio – can touch the track record of Renaissance Technologies founder Jim Simons. Since 1988, Renaissance’s signature Medallion fund generated average annual returns of 66 percent. The firm has recorded trading gains of more than one hundred billion dollars. Simons himself is worth twenty-three billion dollars.”
While the story is remarkable, there are also a few important nuances worth considering. First, the returns were generated by aggregating tiny gains from huge volumes of short-term trades, something that would be impossible for most to replicate. Second, the trading gains were amplified by significant financial leverage provided by banks that may not have understood the extent of the risk involved. Third, the fund wasn’t available to the public and instead was only open to employees, friends and family.
Ultimately, the author’s research led him to conclude that Jim Simons’ extraordinary track record among quant funds was the exception, not the rule. Most have produced uninspiring results that haven’t been much better than those of traditional firms doing old-fashioned research.
Sometimes the computer algorithms that drive investment decisions fail dramatically, the most famous example being the fate of Long-Term Capital Management (LTCM). The firm was founded in 1994 by John Meriwether, the former vice-chairman and head of bond trading at Salomon Brothers, and its board of directors included celebrated scholars Myron Scholes and Robert C. Merton, who in 1997 shared the Nobel Prize in Economics.
LTCM was initially successful, with cumulative returns (after fees) of almost 145% in its first three years. However, in 1998 it lost US$4.6 billion in less than four months due to a combination of high leverage and exposure to the 1997 Asian financial crisis and 1998 Russian debt default. These catastrophic losses jeopardized not only the biggest banks on Wall Street but the stability of the financial system itself. Robert Lowenstein’s book, When Genius Failed, explained not just how the fund made and lost money but also how the personalities of LTCM’s partners, the arrogance of their mathematical certainties and the culture of Wall Street itself contributed to the firm’s rise and fall.
It’s clear that some have successfully used AI to create an edge and generate impressive returns from aggressive short-term trading. It’s less clear that AI can predict the future or which investments will perform best over the next 10 or 20 years.
Phoenix-based Saguaro Capital Management has spent nearly a decade applying AI to fundamental investment management. While the company believes their approach yields a competitive edge in identifying great companies, they are also under no illusions about AI’s limitations. They tried and failed to leverage AI to predict five-year company growth rates using historical data. No matter how many times or ways they attempted to build a model to predict long-term revenue growth, they never achieved a result better than random chance. That led to their thesis that the future is unknowable and even AI can’t predict the unknowable.
Humans are emotional animals, and those emotions are an investor’s worst enemy. Our natural biases, including loss aversion, anchoring and herding, cause us to make mistakes that undermine our investment performance. Dalbar Inc. has studied our shortcomings for decades and quantified the cost. According to their calculations, the average U.S. mutual fund investor achieved a compound annual return of just 6.0% over the last 30 years, nearly 40% less than the 9.9% annualized return for the S&P 500.
We are skeptical that AI will help investors overcome the influence of emotional biases. Odds are investors will lose patience when the investment bots underperform. In fact, that is what The Wall Street Journal discovered. The 13 funds identified in their article (referenced above) suffered outflows of US$300 million this year and last. And that is a relatively large figure considering the funds have an aggregate of US$670 million under management.
We have no doubt that AI will be used in the future to produce both outstanding investment returns and managers who are treated like rock stars. However, as is the case historically, there will also be plenty of mediocre results from funds that use AI. Moreover, given human nature and our tendency to overdo good things, the occasional disastrous outcome is likely, too.
Most of us invest for the long term, and if you want to know your retirement money will be there when you need it, and that it will grow over time, then AI-driven day trading is probably not the answer.
Odlum Brown has been around for 100 years, helping clients invest in stable, well-run businesses for the long term. That’s the secret, not trying to outsmart other investors day-to-day. Not everyone invests the way we do, but the results over time don’t lie, and clients who play the long game tend to do better.
In our view, a good investment advisor will always play an important role in helping clients survive market turbulence, avoid emotional mistakes and develop an investment plan that is well tailored to their needs and risk tolerance. We also doubt there will ever be a replacement for the empathy and counsel a real person can provide when navigating life’s inevitable challenges.
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