It’s no secret that a significant percentage of the trades executed on Wall Street are algorithmically controlled. Depending on which data source you reference, that number is anywhere from 70-80% of market activity on any given day. This “rise of the machines” scenario has been unfolding for several decades and is widely accepted as technological “progress,” but is it? Many financial professionals feel it does more harm than good.
If you only look at the math for a single client, applying an algorithm to an investment portfolio seems like a smart idea. Financial advisors want to protect their clients from losses, so they use programs that will automatically “sell” when stocks decline in value or “buy” when they start to go back up. Machines can do that faster than humans. Algorithms allow advisors to take advantage of “speed trading” and essentially “set and forget” client portfolios.
The Effects of High-Volume Algorithmic Trading
Take the scenario above and multiply it by 6.43 billion. That’s how many shares were traded per day on Wall Street back in 2017, the lowest volume in the past decade. 70% of those were algorithmically controlled, so 4.5 billion shares a day moved without the help of a human overseer. Surprisingly, the Dow gained 25.08% that year, lulling investors into believing that machine-based trading was actually working. In an expanding market, they were correct.
Fast forward to the year 2020. An over-supplied and underpriced petroleum market, coupled with economic shutdowns during the Covid-19 outbreak, caused share prices and futures to go down. Some advisors recommended that clients refrain from selling and evaluate the long-term picture. Machines don’t reason like that. Share prices went down. Algorithms triggered the trades. Massive selloffs sent the market into a tailspin.
Pause for a moment to digest this. An oil price war and a worldwide pandemic would cause the Dow and S&P to fall even without algorithmic trading, but would the damage have been minimized if human beings were flying the plane? The same algorithms that caused the market to drop also helped facilitate a recovery the following week. This raises the argument that high frequency trading might accelerate volatility, but long-term growth is still attainable.
Market Correction or Bear Market
One of the quandaries faced by financial analysts is how to classify market conditions when algorithmic trading causes a selloff. A 10% decline in market indexes is considered a “market correction.” This usually happens when stock prices are artificially inflated by optimistic projections that are not met. A “bear market” is a 20% decline and can be a warning sign for an upcoming recession. At the end of Q1 in 2020, the US stock market dipped into bear territory.
To put this in perspective, the market had been in “bull market” mode since 2016, with consistent increases in stock prices since the current administration took office. Even with the losses in the first quarter, April 2020 begins Q2 with a stock market value that is roughly equal to that of April 2017. We took a big hit, but the numbers are still okay.
Market Circuit Breakers Can Halt Volume Trading
On October 19, 1987, the day known in financial circles as “Black Monday,” the Dow fell 22.6% during a day-long nightmare of “panic selling” around the world. The trading volume was so large that the computers and communication systems at the time were unable to handle the sell orders. Updates were delayed, wire transfers didn’t go out on time, and orders were left unfilled for an hour or more at times during the day.
President Ronald Reagan, after the Black Monday debacle, convened the Brady Commission and gave them as one their directives an assignment to create “financial regulatory instruments” to prevent dramatic price swings and allow sufficient time for traders to complete their transactions. Shortly thereafter, the NYSE and other stock and commodities markets around the world installed what is known today as “circuit breakers.”
Market circuit breakers are designed to stop trading when the S&P 500 Index drops by 7% (Level 1), 13% (Level 2), and 20% (Level 3). If one of these is triggered, all trading stops for a few minutes to allow orders to catch up. If it occurs close to the end of the day, the market closes early, which happened on October 27, 1997. Circuit breakers were also triggered on March 9, 2020, when the Dow fell by 7.79% in early morning trading.
There’s No Substitute for a Human Being
When one computer has to trigger a halt in high volume trading which was initiated by another computer, we might want to look at the system and evaluate what we’re doing. There’s no substitute for a human being. If we manage wealth based strictly on what the algorithm says we should do, why do we need financial professionals? It’s no wonder that clients are leaving their advisors to use robo platforms.
The Covid-19 crisis has created a dire economic environment that no one was fully prepared to deal with. It certainly wasn’t programmed as a variable in the trading algorithms, nor could it be, even if we wanted to. Our entire world is in unfamiliar territory. It will take human intelligence to help us survive this crisis and get to the other side of it with our financial futures intact. Machines don’t think or care. People do. Let’s utilize more of them.
Kevin D. Flynn is the founder and CEO of AdvisorScale Financial Writing. When he’s not writing or on the golf course, he spends his “free” time designing WordPress websites or creating business sales processes for start-ups. In addition to AdvisorScale, Mr. Flynn is also the Executive Director of H.E.L.P. for Young Readers and Managing Editor at October Golf Magazine. He lives in Leominster, Massachusetts, with his wife Evelyn. They have four adult children, two cats, and eight wonderful grandchildren.