The Market Won’t Wait: How Retail Investors Fall Behind Automated Trading Systems

In 1999, E*TRADE aired a memorable commercial that opens with an office worker sitting at his desk when he notices an incoming message pop up on his PC. Curious, he opens it and discovers that a stock he owns has skyrocketed. He leaps from his chair in joy, races upstairs, barges into his boss’s office, and triumphantly announces that he’s quitting.

But the punchline comes fast. As he strolls back to his desk, basking in his newfound freedom, he settles into his chair and glances back at his screen. To his horror he sees that the very same stock has crashed back to earth. His moment of triumph lasted mere minutes. The opportunity evaporated just as quickly as it arrived.

The ad perfectly captured the "get rich quick" sentiment of the day in a fun and amusing way, but the truth behind that message was deadly serious:

Markets move faster than we think.

And that was 1999. Today’s Market Moves Even Faster. Consider this:

A significant portion of all U.S. equity trades are executed by algorithms, and it's growing.

According to a 2024 Coalition Greenwich report, approximately 37% of all U.S. equity trading volume in 2023 was executed through algorithms or smart order routers. And that was months ago — a snapshot of a market already in motion. Since then, algorithmic participation has almost certainly climbed even higher, and the trend shows no signs of slowing. These systems ingest massive amounts of market data, analyze patterns, make decisions, and execute trades all in milliseconds, leaving old school retail traders flatfooted.

So what should an investor do? Stick with buy‑and‑hold? If your goal is slow, steady growth, then a diversified long‑term portfolio can indeed be a path to wealth, provided it’s applied consistently over many years and supported by a healthy economic backdrop. I don’t fault anyone who chooses this route. In fact, a majority of Americans who own stock do so through long‑term, buy‑and‑hold vehicles like index funds and retirement accounts. I allocate a portion of my retirement portfolio to this strategy as well. But the approach is not without its risks. History shows that major market downturns can take years — sometimes the better part of a decade — to recover. After the Dot‑Com Crash, the S&P 500 didn’t reclaim its 2000 peak until 2007, and following the 2008 financial meltdown, it took until 2013 for the index to fully recover. 

What’s more, traditional buy‑and‑hold strategies and index investing often leave money on the table. A growing body of research has shown that systematic, rules‑based quantitative strategies — even those that don’t rely on machine learning — have consistently outperformed passive index approaches across multiple asset classes and market regimes. For example, Asness, Moskowitz, and Pedersen demonstrated that core quantitative factors such as value and momentum generate persistent, statistically significant excess returns in markets around the world. Likewise, Fama and French’s expanded five‑factor model shows that systematic factor exposures explain the majority of long‑term equity performance, reinforcing the idea that disciplined quantitative methods can deliver superior results compared to simple buy‑and‑hold index investing. 

In other words:

Most people are all-in on a slow, underperforming strategy in a market dominated by algorithms.

This creates a structural imbalance for the retail investor:

• Algorithms never sleep.

• They never hesitate.

• They never get distracted.

• They never miss a signal.

• They never wait until a meeting ends to place a trade.

On the other hand, retail traders and investors live in the real world. They have jobs. They have families. They have commutes, errands, and responsibilities. They rarely have time to perform the necessary research to support their investment decisions or navigate the daily influx of information that can pull the carpet out from under their positions. 

Meanwhile, Mr. Market doesn’t pause for any of it. 

Interestingly, retail investors who try to outsource their analysis — by subscribing to investment newsletters — still underperform. Decades of data show that the problem isn’t usually the recommendations themselves, but the human bottleneck between receiving the advice and acting on it. Subscribers often delay execution, second‑guess the guidance, abandon positions prematurely, or fail to follow the strategy consistently. The result is predictable: the average investor dramatically underperforms the very strategies they attempt to follow. The DALBAR Quantitative Analysis of Investor Behavior has repeatedly shown that retail investors’ mistakes, timing errors, and inconsistent adherence to recommendations lead to chronic underperformance in the range of 5 - 7%, even when the underlying advice is sound.

For retail investors that do manage to carve out time to research markets, identify opportunities, and follow a strategy — again, a tall order for most — the real challenge begins when a trading signal actually appears. More often than not, that moment arrives at the worst possible time: during a meeting, while driving, in the middle of a school pickup, or while juggling a dozen other obligations. By the time they notice the alert, think it through, log into their brokerage account, and manually enter the order, the window of opportunity has already begun to close.

And that delay isn’t trivial. Research from MIT’s Laboratory for Financial Engineering shows that the gap between receiving a signal and executing a trade is one of the largest sources of slippage for retail investors — not because they lack intelligence or discipline, but because the market moves faster than human logistics allow. Every minute spent second‑guessing, hesitating, or navigating a trading app represents opportunity cost in a market that can move sharply in seconds.

In short, the retail investor is structurally disadvantaged not only by slower information but by slower and inconsistent execution.

The E*TRADE commercial that opened this article was meant to be humorous, but its message has aged into a warning. In a market where algorithms dominate volume, machine‑learning systems react in milliseconds, and price movements compress into ever‑shorter windows, retail investors are effectively pushed into broad‑market index strategies — not because they’re optimal, but because they’re the only approach that doesn’t require constant vigilance. The alternative is to attempt active trading in an environment where the competition never sleeps, never hesitates, and operates at speeds no human can match. It’s a choice between a strategy that often underperforms and a battlefield where the odds are stacked against them from the start.

This is why automation is so important, and it isn’t a luxury anymore; it’s the only way to operate on the same playing field as the systems that now drive the market. Automation removes the very bottlenecks that consistently punish retail investors — hesitation, distraction, second‑guessing, and the simple reality that life gets in the way. It executes instantly when signals appear, eliminates the opportunity cost created by human delay, and enforces discipline in a way that emotions and busy schedules never can. In a market defined by speed, automation restores fairness by giving individuals the ability to act with the same precision and immediacy as the algorithms they’re competing against. 

This isn’t your father’s retail trading world. Today, speed in both decision‑making and execution isn’t just an advantage — it’s the difference between participating in the opportunity and watching it disappear.

Notes:

1. Bray, Wesley, “E-trading platforms experienced increased share of US equity trading volume in 2023”, THETRADE, January 17, 2024, https://www.thetradenews.com/e-trading-platforms-experienced-increased-share-of-us-equity-trading-volume-in-2023/?utm_source=copilot.com.

2. Asness, Clifford S., Moskowitz, Tobias J., and Pedersen, Lasse Heje. “Value and Momentum Everywhere.” The Journal of Finance, 2013. https://doi.org/10.1111/jofi.12021

3. Fama, Eugene F., and French, Kenneth R. “A Five‑Factor Asset Pricing Model.” Journal of Financial Economics, 2015. https://doi.org/10.1016/j.jfineco.2014.10.010 (doi.org in Bing)

4.  DALBAR, Inc. “Quantitative Analysis of Investor Behavior (QAIB).” Annual Study, 2023. https://www.dalbar.com/QAIB

5.  Lo, Andrew W., and Zhang, Jiang. “The Growth of Indexing and the Need for Better Trading Cost Measurement.” MIT Laboratory for Financial Engineering, Working Paper, 2019. https://lfe.mit.edu/wp-content/uploads/2019/06/LFE-Indexing-Trading-Costs.pdf