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Quant Investing’s Biggest Edge

  • Quantitative investing uses complex computer analysis to process tons of data and reveal patterns that would otherwise be invisible to humans.
  • But, as Nicholas Vardy explains, the real edge for quant and swing trading is a lot simpler.

Editor’s Note: In case you missed our exciting news last week, I wanted to share that our brilliant ETF Strategist Nicholas Vardy is transitioning into a new role as The Oxford Club’s Quantitative Strategist. What exactly does that mean? Well, Nicholas will be taking a deeper dive into quant investing strategies and trends, and sharing that knowledge with our Liberty Through Wealth readers and his subscribers.

Take a look at today’s article to learn what Nicholas considers quant investing’s biggest edge for investors…

– Christina Grieves, Senior Managing Editor


Ask any quant investor what his greatest edge is, and he’ll probably tell you it’s his computer’s ability to process vast amounts of data. That, in turn, allows the computer to identify patterns invisible to the human eye.

I disagree.

I’ve spent the past six months immersed in the world of quant investing, so I appreciate the power of computers to churn through reams of information.

But I don’t believe that raw computing power is quant investing’s most significant edge.

Instead, its edge is in offering a solution to the one problem even the most complicated mathematical algorithms can’t solve: the problem of human psychology.

Let me explain…

Investing’s Psychology Problem

When you begin your journey as a professional investor, you focus on a handful of skills.

You analyze financial statements. You learn to value companies. You master the Black-Scholes option pricing model.

Then one day, you have an epiphany.

You realize that successful investing is more about mentality than math.

The “current state of the market” is one giant Rorschach test.

What we perceive says more about us than what is really there. And chances are, our perceptions are distorted.

Black Ink

Modern finance has only started to acknowledge the importance of psychology through “behavioral finance.”

And that’s thanks only to Daniel Kahneman, the Princeton psychologist who won the Nobel Prize in economics in 2002. (Kahneman never took a single course in economics in his life.)

Behavioral finance takes the assumptions of modern finance and unceremoniously chucks them out the window.

Behavioral finance is all about the psychology of decision making.

Its fundamental insight is this: Humans suffer from a wide range of cognitive biases.

Any of these alone make it extremely difficult to win in the markets. Together, they make it almost impossible.

See if you recognize yourself in any of these…

Confirmation bias: You tend to search for new information in a way that confirms your preconceptions. You shoehorn every market behavior into your preexisting model of the world. You shut out interpretations that contradict your existing beliefs.

Need-to-understand bias: You must have an explanation of what is going on with the markets. You blame the market’s behavior on Trump, the Democrats, the coronavirus. The list of possible reasons is endless. You create what Nassim Taleb has called a “narrative fallacy.”

Need-to-be-right bias: You’re smarter than the average bear. You have a Wharton MBA and 25 years of experience. You have conducted a thorough analysis, and you have an answer. You have a deep-seated need to be right. You don’t realize that successful investing has next to nothing to do with the quality of your analysis. It has everything to do with how you handle being wrong.

Degree of freedom bias: You are a trader with an engineering background. You’ve designed the perfect trading system. You’ve back-tested it and confirm you would have made 1,200% last year. You believe the more rules your system has and the more “degrees of freedom” you incorporate, the better the results. You fall into the trap of over-optimization.

What do all these biases have in common?

They reject homo economicus – that perfectly rational actor you meet in every finance textbook. Yet, like Bigfoot, he has never been found in real life.

Quant Investing and Excising Human Psychology

I designed a trading system for the very first investment fund I managed in the 1990s. I had to load an IBM PC with the historical data from a CD.

It was only then that I could screen stocks, generate charts and back-test systems based on historical data. The combination of data and computing power allowed me to uncover anomalies in the market invisible to others.

Today, I have access to far more complicated software. These are programs that PhDs at the world’s top hedge funds would have spent tens of thousands of hours developing 20 years ago.

And over the past six months, I’ve been developing several “swing trading” systems to profit from short-term moves in the market.

These “swing trading” systems target double- and even triple-digit gains over two to 10 days.

The results so far have been better than I expected.

Most recently, the systems triggered dozens of buy signals on Friday, February 28. This was at the very time when human traders were fleeing the market.

Sure enough, the U.S. stock market rallied more than 5% on March 2, the very next trading day.

The lesson?

Quant systems aren’t human. They are not driven by fear, greed or any other emotion. Removing human psychology from the investment equation is the real edge of quant investing.

Good investing,

Nicholas


About

An accomplished investment advisor and widely recognized expert on quantitative investing, global investing and exchange-traded funds, Nicholas has been a regular commentator on CNN International and Fox Business Network. He has also been cited in The Wall Street JournalFinancial TimesNewsweek, Fox Business News, CBS, MarketWatch, Yahoo Finance and MSN Money Central. Nicholas holds a bachelor’s and a master’s from Stanford University and a J.D. from Harvard Law School. It’s no wonder his groundbreaking content is published regularly in the free daily e-letter Liberty Through Wealth.

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