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To Make Investments Humans Can’t, Firm Uses Models

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Wes Gray, managing director at Cleveland-based firm Empiritrage and assistant professor of finance at Drexel University in Philadelphia, is a self-confessed “academic at heart,” which is why he follows closely the new research on behavioral finance coming out of universities.

However, behavioral finance principles also interest Gray because they are key to the quantitative asset management methods that Empiritrage follows.

“We want to avoid all issues associated with behavior,” Gray said.

Empiritrage’s success lies in building quantitative models that will invest in markets and situations that human beings, because they are conditioned by a range of different investment biases, won’t.

“Everyone knows that for the most part, cheap stocks are only cheap because of some short-term investor pessimism, and there is overall evidence to suggest that they are not pieces of junk,” Gray said. “But still, it’s very hard for most investors to go in and buy those stocks, though many may offer value, and most of them aren’t analyzing a particular company’s long-term track record.”

A model can do that, though, particularly one that has been built to screen for and counteract predictable human behavior and investment biases.

“While a human being can’t buy cheap stocks, a computer has no feelings,” Gray said. “We know that there is a history behind stocks, but because we can’t get ourselves to buy them on our own when they’re so cheap, we get a computer to do it for us; all of us, me included, are subject to the same inbuilt biases.”

Making the best investment moves, then, means not only recognizing these biases, but being as far removed from them as possible.  “The computer is the only way to protect ourselves from ourselves, so to speak, because we can succumb to the same behavior as anyone else. If we make decisions based purely on numbers, we can avoid the mistakes everyone else makes,” he said.

Empiritrage’s quantitative models are built on the recognition of behavioral biases and with the intent to capture the returns in the market so as to take advantage of those biases when other people are suffering from them. The algorithms are modeled to counter and take advantage of overconfidence, for example, as well as loss aversion and sadness, among others, all of which are typical behavioral biases that everyone exhibits.

“No matter how financially tough you may be, your investing is going to be affected by sadness because you’re human, after all. The only way to get around that is via a computer that doesn’t feel sad or happy or confident or anything,” Gray said.

The models also use variables like historical performance data, which many investors wouldn’t stop to consider in short-term market downturns. Gray gives the example of Apple.

When the company’s stock was at $700, his model deemed it too costly an investment. When the stock fell to $400, at a time when most investors were wondering about the company and questioning the more qualitative aspect of its leadership, “it popped up because our model screens for cheapness, and Apple at that time was a cheap firm of very high quality,” Gray said. “We know that when we hold a basket of these securities, we are taking advantage of individual investors’ short-term biases.”

With $140 million under management, Empiritrage is up 35% to 40% this year in its Quantitative Value strategy (EQV). The firm specializes in tax-efficient, quantitative investment strategies for family offices, high-net-worth individuals and “investors with a long-term investment horizon,” Gray said, “because this kind of an investment strategy isn’t for everyone.”

Gray and his team are constantly poring over behavioral finance research and studying how different kinds of behavior results in poor investment decisions, in order to be able to come up with algorithms that can get the better of those.

“We’re really not any smarter than anyone else out there,” he said. “We just use computers to take advantage of their weaknesses, and we keep our hands off, because we also know that the minute we introduce ourselves into the mix, things would be totally different. We’re also human, after all.”