Day-trading reality check: humans are poor investors and even worse traders

The shocking death of Alex Kearns, a 20-year-old day trader who recently died by suicide, highlights a broader caution to young people: do not get sucked into digital trading platforms — no matter whether they have noble-sounding names or are “free.” You will most likely lose your money or worse. There are better ways to make money.

With the exception of people like Warren Buffett, humans are poor investors and even worse traders. Sure, the occasional human might get lucky, but in general, the odds are heavily stacked against you. Unless you have some special information or expertise, you are best off investing in a market index as early in life as possible and enjoying the benefits of compounding.

Read:The rise of mom-and-pop investors in the stock market will ‘end in tears,’ warns billionaire Cooperman

I have been teaching and engaging in systematic investing for over 20 years. My core message to all students and professionals is to not overestimate their competence or the quality of their beliefs, but to continually challenge them.

The second reason for caution is more sinister. It involves the “objective functions” of the platforms where you park your money. How do they make money if they are free to users?

Digital trading platforms make money through a complex web of rebates for funneling trading activity downstream to various venues, and collecting interest on money flowing through the system. Their objective is to therefore maximize the flow of dollars through the system, period. All accounts of any size are welcome. How you perform is largely irrelevant to their business model as long as there are some “intermittent rewards” for the user, like a winning trade. Indeed, the experience created is one of gamification. It is fun, like being in a casino, which is pumped with oxygen to stimulate flow. As a former designer of Google recently remarked “if you’re an app, how do you keep people hooked? Turn yourself into a slot machine.”

But digital platforms are worse than casinos, where most games are relatively simple and easy to understand. And the casino doesn’t loan you money to make your bets.

The trouble is that most people, including professionals, don’t often understand the subtle but important nuances of the financial products they trade, which increase in complexity by the day. Many products, for example, provide “free leverage,” like a triple-levered version of the SPDR S&P 500 ETF Trust
an exchange-traded fund that tracks the S&P 500 index. A common misconception is that the triple-levered version, which is called a “derivative” product, will result in triple the performance of the single-levered ETF. In reality, however, performance can diverge considerably even over a few days, depending on how the product is managed, which is typically in fine print that retail investors don’t read. The marketplace is full of ways to harm yourself.

A student from my most recent Systematic Investing class at New York University gleefully shared how the class had helped him make 150% on his investment and pay off his student loan. I congratulated him, but told him he could just as easily have lost more than that amount, and to be cautious about leverage. A less cheerful account from 10 years ago involved a more experienced trader, whose family money was wiped out during the flash crash of May 2010 due to how his orders were executed. He never recovered it.

The bottom line is this: don’t trust digital platforms that appear to be “free.” You will pay the price one way or another and may not be aware of it. Over the long run, the more you trade, the more you will lose. And do not trade products you don’t understand, especially if they involve fine print.

But what if you really want to trade? Perhaps it is an addiction you cannot control. Perhaps it is the rush of making money, or engaging with the markets for its own sake and taking risk intelligently. In this case, one path I recommend is to apply the scientific method to the problem using large amounts of data. This requires a conceptualization of the problem, hypotheses, data and algorithms. Specifically, it requires a process that is applied consistently to the data and is not impacted by emotions or preferences. This is more involved in terms of setup than making discretionary day-trading calls, but if it done properly, will provide you with outcomes that are based on applying a concept consistently instead of becoming a victim of fear or greed.

A second path I recommend requires an analysis of fundamental factors like the economy or the company’s business prospects. For example, there are significant opportunities created by crises like the current pandemic. We might analyze, for example, what changes COVID-19 will induce in human behavior that are likely to be permanent.

One such irreversible trend is “virtualization,” which favors entities and sectors where products and services can be delivered digitally, and punishes those with large physical assets and heavy debt burdens. In my analysis, I drew parallels with the previous crisis of 2008-09 and teased out what is likely to be different about the recovery this time. For example, commercial real estate rebounded incredibly strongly after the financial crisis, but this would be surprising with the increase in remote work.

Such an analysis can be supported by data, but there is no getting around the hard work of poring through financial statements and assessing economic trends, and then picking the investments most likely to profit if you are right about your assumptions.

There are very few things in life that are more important than money. Acquiring it is difficult and growing it is challenging. The last thing you want to do is gamble. Do not trust the “objective functions” of digital trading platforms since their objectives are unlikely to align with yours. Think deeply and invest wisely.

Vasant Dhar is a professor at New York University’s Stern School of Business and the director of the Ph.D. program at the university’s Center for Data Science. He is the founder of SCT Capital Management, a machine-learning-based systematic hedge fund in New York City.

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