Slugging It Out in the Equity Arena

“No más.” One of the most memorable boxing matches in history ended when those two words were uttered by Roberto Durán in the eighth round of his title rematch with Sugar Ray Leonard in 1980. Nearly 34 years later, Durán’s reasons for walking out of the ring remain a matter of speculation and controversy. Was it stomach cramps? An injury? Or simply frustration at being outclassed by an opponent he had beaten soundly a scant five months earlier? Regardless, his quitting sent shockwaves through his native Panama and the entire boxing world. For Durán was the quintessential tough guy. He grew up on the hard streets of Panama City and became a professional fighter at the age of 16; his brawling style spawned the nickname “Manos de Piedra” (hands of stone). If there were “a least likely to quit mid-fight” award, Durán would win it hands down. But, whatever the reason, he just couldn’t take it any longer. 

Like prizefighters, investors can take quite a beating. Sometimes the blows are absolute, catastrophic losses. But more often the jabs and uppercuts come in the form of relative shortfalls. Fortified with long investment horizons, diversified rosters of managers, limited short-term liquidity needs, and ample risk tolerances, most investors should be tough enough to absorb the punishment. But all too often they, too, are sorely tempted to give up the good fight and abandon their convictions in the middle rounds. Hope fades, and they sell the stocks that have lost value. Desperation sets in, and they buy stocks that have already appreciated, on the chance they might continue to rise. In short, they quit. 

Selling recent losers and buying recent winners is the antithesis of the systematic rebalancing discipline through which smart beta strategies earn long-term excess returns. Indeed, we contend that this procyclical behavior is what pays, over time, for the value added by fundamentally weighted index investing and other smart beta strategies. 

Smart Betas Trading

To us, the smart beta moniker refers to rules-based investment strategies that use non-price-related weighting methods to construct and maintain a portfolio of stocks.1 The research literature shows that smart beta strategies earn long-term returns around 2% higher than market capitalization-weighted indices. Moreover, smart beta strategies do not require any insight into the weighting mechanism. One can build a smart beta strategy with any stock ranking methodology that is not related to prices, from a strategy as naïve and transaction-intensive as equal weighting to a more efficient approach such as weighting on the basis of fundamental economic scale. For example, a low volatility portfolio and its inverse, a high volatility portfolio, both outperform the market by roughly 2%—as long as they are systematically rebalanced. It is not the weighting method but the rebalancing operation that creates most of smart beta’s excess return. Acting in a countercyclical or contrarian fashion, smart beta strategies buy stocks that have fallen in price and sell stocks that have risen. 

Because all smart beta strategies are inherently doing the same thing—contra-trading against price movements by means of the rebalancing process—they are generally buying and selling the same stocks.3 It seems reasonable to assume they have pretty much the same trading partners. 

To illustrate this commonality, we analyzed the 2013 reconstitution of a fundamentals-weighted index in the light of the constituents’ recent performance. Table 1 lists the top 20 portfolio holdings by weight prior to rebalancing. We evaluated how the stock performed relative to the market over the previous 12 months and noted whether the rebalancing trade was a purchase or a sale. In 90% of the observations, the fundamentally weighted index is buying when the stock underperforms and selling when the stock outperforms.4 To see whether another smart beta strategy would have traded the same stocks in the same direction, we also looked at the transactions that would have been executed by a hypothetical equal-weighted index whose holdings were contained in the Russell 1000® Index. The fundamentals- and equal-weighted strategies bought and sold 80% of the same stocks. These results indicate that smart beta strategies with meaningfully different weighting methodologies tend to engage in similar countercyclical stock trading. 


Who Trades Opposite Smart Beta?

The stock market is an equilibrium market. For every trade, there must be a buyer and a seller, and for one investor to profit another must lose. This simple arithmetic means that investors in aggregate will earn the market return and no more. Indeed, less, because Jack Bogle’s “Cost Matters Hypothesis” is right: “Gross returns in the financial markets minus the costs of financial intermediation equal the net returns actually delivered to investors.”5And if we take away the low-cost index funds, what’s left are the active managers, whose investment management fees are much higher. In Bogle’s phrase, it’s humble arithmetic—index investors will win. Nonetheless, smart beta strategies claim to beat the market by 2%. What gives? 

Clearly, there is no way to determine precisely who trades with fundamentals-weighted and other smart beta strategies. Conceptually, however, investors taking the other side of smart beta trades would be those who buy stocks after they rise in price and sell after they fall. They are trend-following, performance-chasing investors; we consider them “procyclical” investors. Smart beta strategies are countercyclical, periodically rebalancing out of what has been working and into less favored stocks. We believe actively managed portfolios, cap-weighted index funds, and many ETFs trade procyclically, but in a larger sense smart beta strategies’ most important trading partners are the end investors—the clients who channel cash to investment vehicles. 

Procyclical Investors Many investors are procyclical. Russ Kinnel of Morningstar published “Mind the Gap 2014,” updating his seminal 2005 article that compared reported mutual fund performance with the returns actually earned by the average investor. The gap—the difference between the fund’s total time-weighted return and the average investor’s money-weighted return—reflects the value added (or subtracted) by investors’ decisions to move cash into and out of funds. In other words, the gap is the return impact of investors’ market timing decisions (Table 2).6 


The conclusion is not unexpected, but it’s still stunning. Over the past 10 years, the average investor earned a return that was 2.5% worse than the return of the average fund they invested in! The drag ranged from –1.7% for U.S. equities to –3.1% for sector funds. Kinnel’s findings are consistent over different long-term time periods and across all asset classes. Of course, these are retail investors; one might expect to find that they are prone to run as a herd. But several studies reported in peer-reviewed journals suggest that institutional investors are no less susceptible to the same procyclical behavior, resulting in a return drag between 1% and 2%.7 

Investors chase returns. Cash streams into outperforming strategies and equity styles. In particular, more capital is channeled to the managers who recently achieved superior results, and so the managers themselves become de facto trend chasers, adding to their positions in the same stocks or the same type of stock already in their portfolios. The stocks they buy have lately had strong returns—they are, after all, the very stocks driving the managers’ outperformance—and they are probably expensive. 

Clearly, there is a common thread when the returns of all kinds of investors (retail and institutional) in all asset classes (stocks, bonds, commodities, and alternatives) fall behind the long-term returns of the funds they invest in. Why do investors persist in costly procyclical behavior? The short answer, as Chris Brightman wrote in this space last month, is that they prefer to do what is comfortable. Buying winners and avoiding losers is a chronic pattern of financial behavior. 

Client Behavior: A Case Study

For a more focused investigation of clients’ market timing decisions, we looked at a highly reputable asset management boutique with a long track record in value investing. A fund launched by the firm more than 20 years ago has an annualized inception-to-date return that is 1.4% higher than the S&P 500 Index return for the same period. But this exceedingly favorable long-term result came with quite a bit of short-term variability, as one would expect of a value strategy. 

The solid line in Figure 1 shows the cyclicality of the fund’s annualized three-year excess returns, which range between 20% below and 23% above the corresponding S&P 500 return. It comes as no surprise that the fund’s investors responded to these peaks and troughs in relative performance in a most procyclical manner. The strategy’s net asset flows, represented by the columns, track its trailing excess returns quite closely. Higher excess returns are followed by net asset inflows; lower excess returns induce outflows. Moreover, inflows tend to continue rising after performance has peaked (investors buy high), and outflows tend to be greatest after performance has hit bottom (investors sell low). 


Smart beta strategies are not taking their 2% excess returns directly from this or any other financial intermediary. As we noted, the long-term excess return of the fund used in this example is positive. Instead, smart beta strategies are earning their value-added returns from end investors whose procyclical behavior forces the manager to sell stocks in bad times (usually when they are at the bottom of a cycle and cheap) and buy stocks in good times (when these stocks have outperformed and are expensive). Smart beta managers and other countercyclical or contrarian investors are the counterparties to those trades. 

We took one more step and looked at the largest positions held by the value fund in question at the end of 2008 (Table 3). These are stocks they might have been compelled to sell during a period of significant outflows. We further observed that, in the course of rebalancing early in 2009, a fundamentally weighted U.S. large company strategy purchased two-thirds of the value fund’s top holdings (the shaded companies in Table 3). And we found much the same phenomenon with other managers who similarly experienced procyclical client behavior. Smart beta strategies were buying the stocks these managers were forced to sell. This is the 2% excess return payment in action. 


Procyclical Investing: Can It Last?

In the United States, the sport of boxing has lost much of the popularity it still enjoyed when Sugar Ray Leonard squared off against Roberto Durán. Certainly there are fewer local boxing gyms to supply new generations of trained fighters, perhaps because nowadays many parents encourage their children to take up less dangerous sports. Procyclical investing, however, is as prevalent as ever. To be sure, procyclical investors can prevail over many rounds, and when they win their behavior is reinforced. But researchers in the field of behavioral finance have also identified a series of cognitive biases and psychological inclinations underlying investors’ penchant for going along with the emotional crowd. They include overconfidence, a compelling need for social validation, and a confirmation bias that leads people to discount or disregard contrary evidence. Because it is hard for people to change their habitual ways of thinking, we do not anticipate a mass movement toward countercyclical investing anytime soon. And as long as investors persist in following trends and chasing returns, someone will throw in the towel and take the other side of smart beta trades. 


1.      Research Affiliates did not coin the popular term smart beta. We are using it as a convenient way to refer to non-price-weighted, rules-based investment strategies in which periodic rebalancing is the central mechanism for capturing premium returns.

2.      The risk-adjusted returns may differ. For example, over the 1964–2012 period, a portfolio whose stock weights were based on the standard deviation σ of monthly returns over five years (thus, a high-volatility portfolio) had a Sharpe ratio of 0.36. The inverse portfolio whose stocks were weighted by 1/σ (therefore, a low-volatility portfolio) had a Sharpe ratio of 0.47. See Arnott, Hsu, Kalesnik, and Tindall (2013).

3.      This does not mean the trades are precisely the same. Different weighting methods will naturally result in somewhat different rebalancing transactions. Portfolios are also likely to operate on different rebalancing schedules.

4.      We also looked at full portfolios for a fundamentally weighted and an equal-weighted index and found they executed countercyclical trades (buying underperforming stocks and selling outperforming stocks) in 75% of their stock positions at the latest reconstitution.

5.      Bogle (2005), p. 22. Bogle’s emphasis.

6.      Because investment managers generally don’t control the timing and magnitude of external cash flows (that is, investors’ contributions and withdrawals), they quite properly report returns on a time-weighted basis. The investors’ experience, however, is reflected by their individual money-weighted returns, which do take external cash flows into account. Kinnel uses industry aggregate cash flow information to estimate the average investor’s actual return. For more information about time-weighted and money-weighted rates of return, see Bailey, Richards, and Tierney (2007), pp. 724–729.

7.      See Stewart, Neumann, Knittel, and Heisler (2009) and Goyal and Wahal (2008).

8.      We also analyzed the results against an appropriate value style index and found nearly identical results. We chose to show the S&P 500 here because we wanted to display the cyclicality of excess returns against the broad market. 


Arnott, Robert D., Jason Hsu, Vitali Kalesnik, and Phil Tindall. 2013. “The Surprising Alpha from Malkiel’s Monkey and Upside-Down Strategies.” Journal of Portfolio Management, vol. 39, no. 4 (Summer):91–105. 

Bailey, Jeffery V., Thomas M. Richards, and David E. Tierney. 2007. “Evaluating Portfolio Performance.” In John L. Maginn, Donald L. Tuttle, Dennis W. McLeavey, and Jerald E. Pinto, eds., Managing Investment Portfolios: A Dynamic Process, 3rd ed. Hoboken, NJ: John Wiley & Sons:717–780. Reprinted in Philip Lawton and Todd Jankowski, eds., 2009, Investment Performance Measurement: Evaluating and Presenting Results. Hoboken, NJ: John Wiley & Sons:11–80.

Bogle, John C. 2005. “The Relentless Rules of Humble Arithmetic.” Financial Analysts Journal, vol. 61, no. 6 (November/December):22–35. Reprinted in Rodney N. Sullivan, ed., 2005, Bold Thinking in Investment Management: The FAJ 60th Anniversary Anthology, Charlottesville, VA: CFA Institute:127–144. 

Goyal, Amit, and Sunil Wahal. 2008. “The Selection and Termination of Investment Management Firms by Plan Sponsors.” Journal of Finance, vol. 63, no. 4 (August):1805–1847. 

Kinnel, Russel. 2014. “Mind the Gap 2014.” MorningstarAdvisor (February 27). 

Stewart, Scott D., John J. Neumann, Christopher R. Knittel, and Jeffrey Heisler. 2009. “Absence of Value: An Analysis of Investment Allocation Decisions by Institutional Plan Sponsors.” Financial Analysts Journal, vol. 65, no. 6 (November/December):34–51. 

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