The Intelligent Use of Smart Beta     

The Intelligent Use of Smart Beta     

This is the first of a series of articles on smart beta.

By Scott Opsal, The Leuthold Group

Understanding Factor Returns And Market Conditions

Quantitative investing has become an integral component of professional investment management, and smart beta funds have become popular vehicles for advisors as they assemble actively-managed client portfolios. Not only have quantitative firms grown in stature, but many fundamental managers are now including factor analysis as part of their overall decision-making process. Likewise, investment advisors and their clients are recognizing the potential for factor-based funds to serve as satellite or tactical positions in a balanced portfolio. We believe it is important for all investors, professional or individual, fundamental or quantitative, to deepen their understanding of factor investing and the role it can play in one’s investment activities.

While the popularity of factor investing is at an all-time high, it still suffers from the same affliction that holds true for every portfolio management approach—no investment process works all the time. Every approach performs well in certain market conditions, but performs poorly in other stages of the market cycle. It is incumbent on fund managers to educate their clients so they understand there will be times when even a sound approach will lag the market, and to not panic or bail out at an inopportune time; to think otherwise is folly.

Despite the appeal of rigorous back-testing and rules-based methodologies, factor investing is not immune to occasional bouts of underperformance. Commentators in this space astutely note that new factors see the light of day precisely because researchers have identified a pattern of positive historical returns. After all, factors that may be well-founded but test out poorly on historical returns are never published and quickly end up in the wastebasket. Even though new factors begin life with a running start (becoming popular right after a stretch of outperformance) they have, without exception, encountered periods of underperformance following their entry into the investment mainstream.

With factor analysis representing a relatively new addition to the investor’s toolbox, the challenge is to understand how factor returns fluctuate over time and what particular conditions or market environments influence those cycles.

The Central Question

If factors and smart beta funds don’t work all the time, is it possible to describe the conditions under which they work best? Is it possible to identify signals that indicate whether or not the factor is positioned to perform well in the near future?


Opinions differ about investors’ ability to “time” exposures to certain factors or smart beta tilts, and prominent quant firms such as Research Affiliates (Rob Arnott) and AQR (Cliff Asness) have been leading a vigorous debate on the merits and proper understanding of smart beta portfolios. Our intent in this study is to advance the body of knowledge in this field by providing an in-depth analysis of the ebb and flow of factor returns.

While researchers have identified literally hundreds of factors that purport to outperform the market, the industry has gravitated to a fairly short list of categories that appear robust across multiple research efforts and slight variations in factor definitions. We will focus our attention on the factor groupings shown in Table 1.

Table 1

Factor Categories





Low Volatility


Stage 1: Factor Definitions

Within each broad category, we begin by testing a variety of factor definitions to determine which variations or flavors of the main theme seem to perform best. Some versions of a general factor will outperform others, and our goal is to identify the definitions that portfolio managers might wish to utilize if they are looking to add a factor component to their investment process.

In the next, and possibly more intriguing, angle of our research we examine each factor’s historical level and return pattern. We aim to test several hypotheses that link a factor’s fundamental characteristics with its returns.

Stage 2: How Do Style Cycles Impact Factor Returns?

In this step, we examine the impact of the market’s overall style cycle on each factor’s return. For example, how does the broad Value/Growth cycle impact the reliability and success of Value factors themselves? In this context, we attempt to determine if the relative merits of different Value factors work better in a Value-driven market than in a Growth-driven market. Alternatively, are there Value factors that work better than others when Growth is dominant and the entire notion of Value is out of favor?

Stage 3: How Do Factor Metrics Influence Performance?

Next, we examine whether the factor’s signal strength is a reliable indicator of future performance. We define signal strength as the difference in the factor’s median or average level for the first quintile of companies compared to the fifth quintile, as sorted by the target factor.

For example, if our factor under consideration is Return on Equity, the signal strength would be defined as the ROE of the top quintile minus the ROE of the bottom quintile. Our hypothesis is that if the factor itself is successful over time, the width of that spread should relate to the performance of the factor. A wide signal spread could be inferred to suggest that the highest ROE stocks offer relatively more of the desired trait and may outperform going forward; a narrow signal spread suggests that the difference between high and low ROE companies is modest, diminishing the scarcity value of high ROE companies and implying that the ROE factor would have less influence on future relative performance.

Stage 4: How Do Valuation Spreads Influence Factor Performance?

Finally, we examine whether the factor’s valuation spread is a reliable indicator of future performance. We define the value spread as the valuation of the first factor quintile compared to the valuation of the fifth factor quintile. As with signal strength (again using ROE as an example), our hypothesis is that in times when the highest ROE quintile is cheap relative to the lowest ROE quintile, the ROE factor should perform well. When the valuation gap across the factor is narrow, we would expect the factor to have less influence on returns. (Note: for Value factors themselves, signal strength and valuation spread are essentially the same thing, therefore our presentation will differ slightly for Value factors compared to all other groups.)

After we conclude our review of individual factors, we’ll wrap up our study by exploring the potential performance gains that may be had by combining successful factors together. We strongly believe that multifactor models are superior to individual factors, and we will explore combinations of attractive factors to see if a mix can extend our performance envelope even further.

As a firm that manages quantitatively-driven portfolios, we have a vested interest in maintaining a deep understanding of factor investing. We believe the proliferation of factor and smart beta styles make it imperative for fundamental managers, financial advisors, and even individual investors to gain the proper understanding of factor-based investment strategies, and we hope this introductory look has peaked some curiosity. As we roll out our research findings, our objective is to provide a sound understanding of factor return cycles and equip readers with the perspective needed to make factor analysis and smart beta useful and profitable additions to one’s decision-making process.

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