Here's How To Exploit 'The Greatest Anomaly In Finance'

If I told you that there is an easy-to-exploit market anomaly that has enabled investors to consistently and substantially outperform the market with less risk for more than four decades, your first instinct might be to roll your eyes. After all, the unending quest to improve returns while lowering risk has yielded countless methods with initial promise that subsequently collapse under further scrutiny.

Not so fast. What if I could show that this market anomaly is well-documented in the academic literature – that it is not just some esoteric theory? And that now some newly created ETFs provide a convenient way for advisors to access this strategy?

You might listen a little closer.

As you might have guessed, this is not actually a hypothetical – I’m referring to the demonstrated outperformance of low-beta and low-volatility stocks.   Since financial theory dictates that lower risk should imply lower return, this is an anomaly.

In this article, I explore the historical evidence for this powerful anomaly, explain what market mechanisms permit it to exist and persist, and examine the ETFs that exploit it to determine whether it is likely that these funds will provide future outperformance. 

“The greatest anomaly in finance”

Support for the effect I describe comes from a recent research paper by Malcolm Baker of Harvard Business School and his co-authors, who called it the “greatest anomaly in finance.”  While financial theory dictates that the only way to achieve higher returns is taking on more risk, this has not been historically true for equities.  In their 2010 paper titled Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly, Baker and co-authors Brendan Bradley of Acadian Asset Management and Jeffrey Wurgler of NYU Stern School of Business found that selectively investing in portfolios of either low-beta or low-volatility stocks over the 41-year period spanning 1968 through 2008 would have resulted in annualized alphas of 2.6% and 2.1%, respectively.  The swings these portfolios experienced were also far less extreme than those of the broader market.  If the universe of stocks under consideration is limited to the 1,000 stocks with the largest market capitalizations, the low-beta and low-volatility portfolios generated 3.49% and 2.1% in annualized alpha, respectively. 

As this anomaly has become more well-known, ETFs have been specifically designed to allow investors to exploit this source of alpha.  The natural question that emerges, of course, is whether this source of outperformance will disappear as investors, recognising this arbitrage, will exploit this anomaly until it no longer exists.  The intriguing conclusion of Baker et al. was that this effect is not disappearing. They proposed a theory that explains their observations and made a compelling case for the future attractiveness of low-volatility and low-beta portfolios. 

Baker et al. analysed historical market data from CRSP, a survivorship-corrected historical research database of equities, and found that grouping stocks into either low-volatility or low-beta cohorts resulted in substantial future outperformance. They found this effect both when they looked at a universe of all publicly traded stocks and when they limited the universe of potential investments to the 1,000 stocks with the highest market capitalizations.

Why it exists, and why it persists

Two puzzling facts must be explained in order to justify Baker et al.’s counterintuitive findings. First, why would some fraction of investors might behave so irrationally as to continue to select a class of equities that have historically underperformed (e.g., high-beta equities)?  Second, why don’t sophisticated investors arbitrage this imbalance away by buying up the underpriced low-beta stocks?

The answer to the first question is simple.  behavioural finance has demonstrated overwhelmingly that individual investors have a substantial preference for ‘lottery ticket’ types of returns, in which they have the potential, albeit at very low probability, of receiving a massive windfall.  This is a central finding of behavioural finance, and it has been demonstrated time and again. 

The second piece of the puzzle is more challenging, and its solution was the key innovation in Baker et al.  Many institutional investors, such as pension fund or mutual fund managers, have a mandate to beat a specific benchmark portfolio (typically a market-cap-weighted index) and to  minimize tracking error relative to that benchmark.  These benchmarks give money managers a substantial disincentive to invest in low-beta or low-volatility stocks, even if they believe that these stocks have substantially positive alphas.  The reason is that low-beta stocks, even when they have high expected alpha, will increase tracking error (since, by definition, they are uncorrelated to the index).  As a result, as Baker et al. explain, institutional managers with target benchmarks actually have an incentive to exacerbate the low-beta anomaly. 

Baker et al. were not the first to propose this mechanism, but they were the first researchers to quantify it, in research they published in the peer-reviewed Financial Analysts Journal.  In representative cases, they found that an equity fund manager will not start to overweight a stock with a moderately low beta of 0.75 until the expected alpha exceeds 2.5% per year.  Further, the researchers concluded that a rational fund manager would actually underweight a stock if its alpha is on the order of 2% per year.

This is a stunning conclusion – it shows that those managers systematically reject attractive investments.

An important secondary conclusion in Baker et al.’s paper is that low beta is actually the fundamental driver of outperformance, not low volatility.  A screen for low beta selects many of the same stocks as a screen for low volatility, but the reason why institutional investors will not arbitrage outperformance away is specifically the low-beta character of these stocks.  Recall that the 41-year historical alpha is substantially greater for the portfolios selected on the basis of low beta than for those selected on the basis of low volatility, an empirical observation that buttresses Baker et al.’s explanation of their results. 

Baker et al. calculated the low-beta and low-volatility effects in various ways.  The simplest and most striking result arose when they created a portfolio from the 200 lowest-beta stocks among the largest 1,000 stocks by market capitalisation.  This portfolio was formed using trailing five-year monthly return data and updated monthly.  This low-beta portfolio generated average annual returns that were 2% per year greater than the average for a market index of large-cap stocks, with less risk than the market as a whole.  The alpha that the authors calculated for this portfolio was 3.49%. 

The future of low-beta and low-volatility ETFs

Let’s now turn to the challenge of diagnosing the potential future outperformance of low-beta stocks.  None among the new crop of ETFs that focus on low-volatility and low-beta stocks has enough of a historical track record to draw any meaningful conclusions from their past performance.  We can, however, analyse these ETFs using a number of standard statistical methods, especially by performing forward-looking Monte Carlo simulations using my Quantext Portfolio Planner (hereafter abbreviated as QPP).

Here are the most popular funds currently available for investing on the basis of low-beta or low-volatility:



Benchmark (Representative Ticker)

S&P500 Low Volatility


S&P500 (SPY)

Russell 1000 Low Volatility


Russell 1000 (IWB)

Russell 1000 Low Beta


Russell 1000 (IWB)

Russell 2000 Low Volatility


Russell 2000 (IWM)

Russell 2000 Low Beta


Russell 2000 (IWM)

The S&P 500 Low Volatility Index ETF, offered by PowerShares, is designed to follow the S&P Low Volatility Index.  The index holds the 100 stocks among the S&P 500 with the lowest volatility.  The weight of each stock within the index is determined by the inverse of its volatility.  The index uses trailing one-year volatility, calculated from daily returns, and it is recalculated and rebalanced quarterly…

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