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Enormous amounts of money are being invested on the assumption that algorithms can consistently identify security mispricings. I estimate that more than $1 trillion is currently committed to smart-beta products utilizing quantitative, factor-based approaches.
Smart beta’s allure is easy to grasp. In addition to offering investors the prospect of above-market returns, it enables asset managers to essentially automate alpha by replacing an army of analysts with an algorithm tweaked by a team of tech-sperts. For an industry in constant pursuit of scale, this is the Holy Grail.
But will it deliver? How likely it is that smart beta proves to be, well… smart?
Excess investment returns are achieved by capturing the difference between the current price of a security and its intrinsic value. As the name suggests, “quant” funds believe that they can consistently identify mispricings by subjecting information to quantitative analysis.
The thing is, numbers alone are dangerously misleading.
For example, what does the P/E ratio tell you about a company and whether it is over- or underpriced? Nothing. Without an in-depth understanding of the nature and quality of the “E,” the ratio is meaningless. The same holds for price-to-sales/cash flow/book value, etc. The relevance of all of these ratios is dictated by the quality of the denominator, which cannot be assessed in purely quantitative terms.
If you were to make a list of what is most important to understand about a company, subjects such as culture and competitive advantage would be at the top of the list (if that doesn’t seem self-evident, try finding a great company with a lousy culture and no moat). What do numbers alone tell you about the nature of a company’s culture or competitive advantage? For example, a firm’s return on invested capital may provide some insight into the latter, but it also overlooks an abundance of critical information. In late 2007, did any of Nokia’s financials indicate that its phone business was about to be devoured by Apple’s iOS and Google’s Android mobile operating systems?
There is a reason why no private equity fund, LBO shop or corporation has ever made an acquisition decision based upon an algorithm or by relying exclusively upon quantitative information. However integral financial data is to their analysis, they understand that the numbers constitute only one part of a vast economic picture. The map is not the terrain.
If making a fully informed decision is the hallmark of intelligent investing, purchasing shares based on an algorithm is not investing it all, but simply trading securities based on statistics. The smart-beta proponents might reply, “Exactly! We’re not investors. Our only goal is to generate profits by trading securities. We don’t care what we own, and neither should investors. Profit is all.”
But what competitive advantage do the quants have?
Even if we suspend our skepticism and accept that it’s possible to develop an algorithm that can crank out profits like playdough, allocating to a smart-beta fund only makes sense if we can also assume that no one else will arrive and ruin the party. And with universal access to computing horsepower and no shortage of PhDs, how likely is it that no one else cracks the code? As long as the reward of obscene profits exists, unless one firm corners the market on quant PhDs, most smart-beta will be hunted into extinction.
None of the foregoing is to suggest that generating modest, incremental alpha is impossible. But it is transient. The lack of anything resembling a permanent competitive advantage means that even the best-performing algorithm will have to constantly be modified and improved. Over the long term, smart beta can only deliver if it can continue to become smarter. Smart-beta investors aren’t betting on an algorithm per se – they’re betting that the funds’ scientists can continue to improve their algorithms.
In lieu of maintaining an elusive analytical edge, many quants have tried to outperform their peers by exploiting the execution infrastructure of a market that can to an extent be gamed – out-trading the competition may be more feasible than outsmarting them. But again, the barriers to competition are very porous, and the quants are reaching for every edge the latest technology might afford. It didn’t take long for this tech arms race to reach its logical extreme, as the various actors struggle to surmount the physical limitations imposed by the speed of light itself (enter the quantum quants).
And then there’s the problem posed by the investors themselves.
Regardless of the investment strategy, in the long run the quality of a fund’s returns rarely exceeds the quality of its capital. In other words, it’s hard to be smarter than the capital you employ.
And with no clue as to the inner workings of the black box, smart-beta investors are flying blind by design. They have only one basis for determining whether the strategy is working or not: performance. Given how little they know, why shouldn’t they take any stutter in returns as a sign to run for the exits? After all, they have little else to go by. It’s not as if they know or remotely understand what they own. They are merely chasing returns. It would be hard to find a better foundation for panic than the extreme complexity and complete opacity of a smart beta fund.
Which begs a final question: Do those overseeing the algorithms understand them any better than their investors?
The artificial intelligence and machine learning employed by algorithms are very effective at identifying patterns embedded within vast data sets. But patterns ultimately reveal nothing about cause and effect; they tell us what happened, but not why. Based upon historical quantitative data, certain factors may appear to generate alpha. But if they can’t explain precisely why their smart beta works, how can they – or anyone else – be confident that it’s anything more than transient, incidental alpha?
Steven Grey is managing principal of Grey Value Management, LLC, on Singer Island, FL.
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