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Uncloaking the Alpha Machine

How finance professors keep moving the goal posts for active management. 

Samuel Lee, 10/08/2012

This article originally appeared in the October/November 2012 issue of MorningstarAdvisor magazine.  To subscribe, please call 1-800-384-4000. 

In August, hedge-fund AQR researchers Andrea Frazzini, David Kabiller, and Lasse H. Pedersen published a study arguing that they’ve identified Warren Buffett’s magic formula: borrow lots of money at low or negative interest rates to buy low-volatility, low price/ book, high-quality stocks. They even constructed a mechanical strategy that does a passable job of mimicking the guru’s returns, at least in back tests. It’s a fascinating and well-done piece, conducted by credible researchers with impeccable pedigrees. It’s the bleeding edge of research conducted at the intersection of academic and practitioner finance. But at the surface, the claim stretches one’s credulity.

It’s actually not much of a stretch. Decades of research has firmly established that most of active managers’ returns, even seeming outperformance, can be replicated by a handful of factors, which can be thought of as simple mechanical strategies. Finance professors describe discovering one of these factors as the process of turning alpha into beta. In the argot of finance, beta is performance attributed to factor exposures; alpha is what’s left over, unexplained, often interpreted as evidence of skill. The professors hold the reasonable notion that once they offer convincing evidence that they can replicate a manager’s outperformance using simple, mechanical rules, and they disseminate the rules widely, the manager no longer deserves to command high fees. The operative word here is convincing—anyone can explain away a manager’s outperformance if they look hard enough.

Factor investing may be news to you, but you’ve almost certainly used it. The growthvalue style delineation, popularized by the Morningstar Style Box, is based on the value factor. Splitting the stock market into growth and value wasn’t just to accommodate investors who needed a simple way of sorting active managers. It was to avoid growth stocks and to buy value stocks. Value stocks, usually defined as stocks with high book-to-price, have outperformed growth stocks in nearly every market studied, over suitably long periods. U.S. large-cap value stocks have beaten large-cap growth stocks in six out of the eight decades since 1930. Over the nearly full sample, 1930 to 2010, they won by about 2.9% annualized. Value index funds, then, can be seen as dirt-cheap computerized strategies to replicate the once-secret sauce value managers possessed.

(Note: The performance data above actually starts in 1927, but for simplicity’s sake I chose to stick with round decades. Large-cap value stocks’ outperformance shrinks to 2.3% annualized if you look at the period 1927–2011. The data are from the Kenneth French Data Library.)

Evidence of persistent outperformance beyond factor exposures is rare. The implication is that many fund managers are unknowing members of a cargo cult: The visits to company managements, the poring through financial statements, the chart gazing are useful to the extent that they touch upon these factors. Why not cut out the middle man and own the factors directly? It’s a question some big institutions have begun asking themselves. The Government Pension Fund of Norway, the biggest pension fund in Europe, and CalPERS, the biggest public pension fund in the United States, have embraced the risk-factor based view of the world. Advisors should begin asking themselves whether they should, too.

An Abbreviated Statistical Detour
What exactly is a factor? In its raw form, it is simply a series of returns. Usually, it’s generated by sorting securities by some metric, and owning a set of the highest-scoring and short-selling a set of the lowest-scoring, ideally creating a market-neutral strategy that reflects a spread return. It may seem strange that one can divine whether a manager is really just “loading up” on a factor just with this information and the manager’s returns.

The intuition is simple. Let’s say I have a machine, whose workings are a mystery. Every day, it outputs either “true” or “false” in a seemingly random fashion.

Mystery Machine T T T T F T T F F T F F T F F T

I also have a handful of machines whose workings I do understand, that also output either “true” or “false” each day.

Machine 1 F T T T F F F T T F T F T T T F
Machine 2 T T T T F T T F F T F F F F F T
Machine 3 T F F T F T F T F T T F F T T T
Machine 4 T T T F T F F F F T F F T T F T

After comparing the outputs of all the known machines with the output of the unknown machine, I notice that Machine 2’s output almost perfectly matches the Mystery Machine’s output.

Mystery Machine T T T T F T T F F T F F T F F T
Machine 2 T T T T F T T F F T F F F F F T

It’s reasonable to infer, at least tentatively, that Machine 2 and the Mystery Machine work similarly. I’d be more confident in my judgment if I looked at 1,000 days of output and saw the similarity persist. In fact, with some basic assumptions, I can calculate the odds at which any matching would be due to pure chance. The Mystery Machine is a manager’s returns; the known machines are factor returns.

This is the intuition that justifies using returns data in factor research. By looking at periodic returns and observing their direction and magnitude, one can see if a strategy’s performance can be “explained” by comparing it to the direction and magnitude of a factor’s returns. In fact, one can see if multiple factors simultaneously explain a strategy’s returns, so one can see if a new factor can “absorb” the explanatory power of another. The tool of choice—the linear regression— isn’t exotic; it’s taught in statistics 101 classes. There are sophisticated variations on this model, but the humble linear regression is robust and powerful, and few experts doubt that it can yield such insights.

Nothing New Under the Sun
Finance professors have the bad habit of attributing discovery of a new factor to the first person who publishes it in a respectable journal. Almost all of these market-beating mechanical strategies have been used by practitioners—wittingly or not—for decades before their “discoveries.” Value investing was largely elevated to its current form in the early 20th century by Buffett’s mentor, Benjamin Graham. The returns from a simple strategy of buying value stocks, usually defined as those having relative low book-to-price, can explain the excess returns many value managers have earned. Besides value, there are three other strategies that most factor researchers agree produce excess returns.

The performance of the broad market. Stocks sensitive to the business cycle possess more of the market factor; less-sensitive stocks possess less. Interestingly, just having some market exposure is more important than having a lot of it. Stocks with high cyclicality have done worse than stocks with low cyclically on a volatility-adjusted basis, both in the U.S. and abroad.

The excess performance of stocks that have done the best over the past one year over stocks that have done the worst. Momentum strategies have been around since markets have existed, but for a long time, they’ve been derided as bunk. Since Jegadeesh and Titman (1993) published a study showing momentum existed in the U.S. stock market, momentum has gained acceptance among academics. It has historically been the strongest and most persistent of the factors and has been found everywhere, along with value. Momentum arguably is what allows value to exist.

The excess performance of small firms over big ones. Size, however, has historically been among the weakest of the factors.

Redefining Outperformance
With the discovery of these factors, academics have raised the hurdle for fund managers. Now, a truly skilled manager has to outperform after deducting the influence of factor exposures to his or her performance. Most studies show that once this deduction is made, evidence of skilled managers becomes hard to detect. Thus, many researchers have concluded that skilled managers are exceedingly rare. Well, that depends on how you think about factors. The researchers who declare the market efficient and evidence of managerial skill as weak usually interpret a factor strategy’s excess returns as compensation for some kind of risk. The father of efficientmarkets, Eugene Fama, argued that value stocks are often distressed (true), and therefore, they need to offer sweeter prospective rewards to compensate investors for bearing additional risk. Behavioral finance researchers point out that value stocks’ underperformance is not obviously connected to recessions, when distressed stocks should do terribly. Depending on whom you find more persuasive, managerial skill is somewhat common (the funds highly loaded on momentum and value with non-negative alphas) or extraordinarily rare.

Regardless of which interpretation is true (a debate that can take on religious fervor), the implication is the same: Managers who mostly load up on factors don’t deserve to charge high fees. Their real competitors are cheap, factor-mimicking funds, of which there are plenty, with more coming down the pipeline. With that said, I won’t be ditching my shares of Berkshire Hathaway anytime soon for a robo-Buffett.


Frazzini, Andrea, Kabiller, David, and Lasse H. Pedersen (2012), ”Buffett’s Alpha,” AQR Working Paper.

Jegadeesh, Narasimhan, and Sheridan Titman (1993), “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency,” Journal of Finance, vol. 48, pp. 65–91.

Samuel Lee is an ETF Analyst with Morningstar.

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