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Why Most Quantitative Investing And Trading Systems Fail

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Why Most Quantitative Investing And Trading Systems Fail

July 28, 2015

by Baijnath Ramraika, Prashant Trivedi

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Advisor Perspectives welcomes guest contributions. The views presented here do not necessarily represent those of Advisor Perspectives.

“Invert, Always Invert.” – Carl Gustav Jacob Jacobi, German Mathematician

“Hundreds of studies have shown that wherever we have sufficient information to build a model, it will perform better than most people.” – Daniel Kahneman (as you read this statement, don’t forget to consider the implication of the word “sufficient”)

“Roger Federer plays tennis using Wilson racquets. I use Wilson racquets. Does that make me Roger Federer?” – Paraphrasing a friend of ours.

In an interesting post, the fund manager Dominique Dassault talked about a time when he was fascinated with quantitative black-box trading systems. As he was talking to a leading quantitative portfolio manager about quantitative systems, the portfolio manager said something that surprised Dassault: While quantitative algorithms may work for a while, even for a long while, eventually, they all just completely blow up. When asked about the reasons for the blow up, here is what the he had to say:

Because despite what we all want to believe about our own intellectual uniqueness, at its core, we are all doing the same thing. And when that occurs a lot of trades get too crowded…and when we all want to liquidate (these similar trades) at the same time…that’s when it gets very ugly.

Dominique went on to offer a good summary of what quantitative managers are doing, including low-enforced back-test volatility, high leverage and increased concentration of risk. All have a very logical rationale.

However, at the core of this problem is a much more basic issue: logical fallacy.

Defining quality – The quantitative way

Most, if not all, quantitative systems are designed by selecting factors that were present in successful investments/trades over the selected back-test period. Typically, a system developer will pick up a host of factors and run simulations in order to identify which factors were associated with better investment returns.

To further expound upon this process, let’s consider the case of quality as an investment factor. It has received a lot of attention by academics as well as developers of quantitative investment strategies. It is the latest fad in the jungle of investment factors.

Most quantitative strategies that promise to utilize quality as the dominant selection factor employ returns on capital or some variation of it. This is driven by the finding that companies that generated higher returns on capital have been associated with higher subsequent investment returns. Of course, as quantitative managers try to step over each other in an effort to showcase the superiority of their system, most of them gravitate towards significantly more complex systems, introducing a multitude of factors in their models.

The idea that a high-quality business generates higher returns on capital passes the muster of commonsense as well. Let’s say that the average return on capital of all businesses is 10%. What this means is that when you invest $100,000 in a business, on average, you will expect to earn US$10,000 from your investment. But what if the business that you invested your $100,000 was earning you $15,000 instead? Most quantitative systems, as they define quality currently, will likely conclude that we have a high-quality business on our hands.

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