They Can’t All Be That Smart
A Due Diligence Framework for Factor Investors

Categories Author: Chris Meredith, Insight, Investing

Not all factor products are smart. This article delineates factor-based strategies — fundamental weighting, smart beta, and Factor Alpha — by showing the differences between them. It also provides a framework1 for determining the alignment between factors and portfolio construction, as well as the fees you should expect to pay. 

Part 1 of this article:

  • Fundamental Weighting
    Weighting on sales or earnings is an indirect value signal, but without controlling for price.
  • Risk-Focused vs. Return-Focused
    Highlights two fundamentally different views of how to implement factors: Smart beta is focused on risk; Factor Alpha is focused on returns.

Part 2 of this article concludes with:

  • Risk Controls
    Risk controls help augment a return-focused Factor Alpha process.
  • Using Active Share
    Active Share can be a useful tool for allocators to understand alignment of alpha signal and portfolio construction, as well as understand appropriate fee structures.

“Smart beta” is a label applied broadly to all factor-based investment strategies. In a recent WSJ article on smart beta, Yves Choueifaty, the CIO of TOBAM, remarked, “There’s a huge range of possibilities in the smart-beta world, and they can’t all be that smart.”2 With the wide range of implementation styles for factor investors, there has to be wide differences in the expected return and risk profiles. This paper separates the factor investing landscape and helps analyze the edges of various approaches.

Analysis of a factor-based investing strategy should focus on two of the manager’s skills: the ability to identify specific factors that accurately generate outperformance and the manager’s technique in constructing a portfolio of stocks with those factors. Factors are not commodities and investors certainly need to be aware of how managers are selecting stocks — but we are focusing on portfolio construction and the soundness of different approaches.

Active Share can be a useful tool in this investigation. Active Share by itself is not a metric that inherently identifies manager skill (nor is it a strong metric for determining the risk of the portfolio versus an active benchmark), Tracking Error is a more comprehensive metric at the trailing differences in the portfolio returns, and an Information Ratio helps investors understand the balance of how much active risk is taken for active return. But, for investigating the choices managers make in building factor portfolios, Active Share is more instructive.

So, through the lens of Active Share, Tracking Error, and Information Ratio, let’s examine the relative merits of 3 factor-based portfolio construction approaches: fundamental weighting, smart beta, and Factor Alpha. Understanding the differences between these approaches will help you successfully incorporate factors into your overall portfolio.

Fundamental Weighting

Most benchmarks weight constituents by market capitalization. Some factor investing approaches pivot away from weighting on market cap, and weighting on another fundamental factor like sales or earnings. The argument for these strategies is that weighting by market cap is not the smartest investment solution out there: the top quintile of the S&P 500 by market cap underperforms the average stock by 0.65% annualized,3 and market cap-weighting allocates 65% of the benchmark to those largest names.

For a comparison of fundamental weighting schemes, the table below shows the characteristics and annualized returns for weighting on Market Cap, Sales, Earnings, Book Value of Equity, and Dividends. There are some benefits to the approach, for example eliminating companies with negative earnings. On average, about 8.3% of Large Stocks companies4 are generating negative earnings, and avoiding those is smart. The largest benefit is an implied value-tilt to the strategy: overweighting companies with strong earnings and average market caps creates an implicit Price/Earnings tilt. This is apparent in the characteristics table: Sales-weighting gives the cheapest on Price/Sales, Dividend-Weighted gives the highest yield, and so on.

But pivoting from market cap to a fundamental factor weighting scheme does not create large risk-return benefits. Raw fundamental factors correlate highly with market cap: companies with huge revenues tend to have large market caps. As of December 31, 2016, weighting on Earnings has a 0.85 correlation with weighting on market cap. In market cap weighting, the top 25 names are 34% of the portfolio. In an earnings-weighted scheme those same 25 companies are still 34% of the portfolio, just shifting weights a bit from one name to another.

Active Share shows how little fundamental weighting moves the portfolios, which have Active Shares in the 20–30% range. Excess returns range from slightly underperforming market cap to outperforming by +72bps. The modest excess return comes with much higher active risk, and tracking errors ranging from 4.5% to 5.8%. This generates poor Information Ratios (the ratio of active return to active risk).

Market Cap-Weighted vs. Earnings-Weighted 5

Characteristics & Performance by Weighting Scheme6

The reason that the risk-return benefits are small is because Fundamental Weighting is an indirect allocation to a Value strategy. Value investing on ratios is identifying investment opportunities with the comparison of a fundamental factor in the context of the price you pay. Fundamental weighting is only taking half of the strategy into account, looking for large earnings but ignoring the price you’re paying for them. Some Fundamental-Weighted products will more sophisticated than simply weighting on sales, earnings, book value or dividends. But weighting on fundamental factors instead of market cap doesn’t create a significant edge.

Risk-Focused vs. Return-Focused

In a Nov-2016 article posted by AQR founder Cliff Asness, he states that smart beta portfolios should focus on “minimizing Active Share.” 7 Also, smart beta portfolios are “only about getting exposure to the desired factor, or factors, while taking as little other exposures as possible.” This statement cemented the idea that there is a group of smart beta products that are risk-focused in nature: start with the market portfolio, identify your skill and then take only the exposure on those factors.

In evaluating this portfolio construction technique, let’s suspend the idea that we’re all starting with unique factors and take a hypothetical example where the skill of all quant managers is a generic factor with only three states: Good, Neutral, and Bad. Most of the stocks (80%) are Neutral and give a market return, while you have an equal amount of Good stocks give an alpha of +4%, and Bad stocks underperform by 4%. To establish some terminology: the strength of the signal is +4% alpha and the breadth of the signal is the top and bottom 10%.


In the risk-focused smart beta framework, you only deviate from the benchmark when you have strong conviction. In this case, start with the market and then “sell” (do not own) the 10% of the market you’ve identified as bad stocks to “buy” (double down on) the 10% you’ve identified as good stocks. For the remaining 80% of the market, you have no edge — so match the market portfolio. The logic seems sound: you’ve maximized the usage of your skill within your risk-focused framework. Only change the stocks you have an opinion on, and if you have no opinion, leave the portfolio at market exposure.

Another equally viable framework is to focus on returns first. Using the same example where there is a group of stocks with an excess return of +4% annualized, a return-focused manager would only own stocks from that group and then try to balance out the risks of the portfolio to match the market’s risk factors. This the “Factor Alpha” approach, which focuses on maximizing excess returns first, and then controls for risks.


Portfolio Construction in Practice

A sensitivity analysis based on a single factor can demonstrate test how the breadth of signal affects the risk-return profile of either approach. The Universe for this analysis is a modified Russell 1000. The market cap-weighting methodology of the Russell benchmark includes a long tail of mid to small cap names. To get around this, only the top 95% of names by market cap are included, trimming a long tail of small cap companies. Portfolios are formed monthly with a 12-month holding period, with analysis on the combined portfolio.

The factor used in the analysis was Shareholder Yield, which is the net return of capital through dividend yield and buyback yield. The following chart shows the annualized returns for portfolios grouped into deciles by Shareholder Yield. There is significant outperformance from the highest shareholder-yielding decile and underperformance from the lowest-yielding decile. The relative performance narrows quickly, with declining utility in the second and third deciles. The returns of the fourth to seventh decile demonstrate little edge and these groups should be considered low conviction.

Excess Return by Shareholder Yield Decile8

Using Shareholder Yield as our basic alpha signal, the analysis was run for both the smart beta approach and Factor Alpha approach, using a different cutoff for the breadth of signal. The universes are the same, and the alpha signal is the same, but we are scaling in how much confidence we have in our alpha signal. For the smart beta approach, we are increasing the active component of the portfolio and reducing the passive component by increments of 2.5%. To be specific, at 10% we have trimmed the top and bottom deciles, equally-weighted the names within the top decile with the combined weight of both groups. For the Factor Alpha approach, we start by purchasing the groupings based on the top 2.5%, and incrementally decreasing the concentration of the portfolio by 2.5%. At 10%, we are only purchasing the equally-weighted top decile, and no other constituents.

In the four graphs below, the excess return and tracking error match our intuitive expectations: the smart beta approach starts with little excess return and little active risk, and both return and risk scale up the more active it becomes. The Factor Alpha approach starts with high excess return and higher active risk, then scales down the broader the portfolio becomes. What’s interesting is that the Information Ratio, the balance of active returns and risk converge fairly quickly. To be fair, for the first few groupings, the smart beta approach is working from a very low tracking error where a shift in excess return of just a few basis points has a significant impact on Information Ratio. But by the time the portfolio gets to the top decile, the Information Ratios from each approach converge. The smart beta and Factor Alpha approaches generate very competitive risk-return profiles, although the overall level of active return and risk are higher in the Factor Alpha Approach.

In both approaches, the Information Ratio then degrades the further you dig deeper into your alpha signal. The reason for the degradation is that benefit of the alpha signal. For Shareholder Yield, the active return drops off more quickly than the active risk, degrading the risk-return profile for either approach. A key aspect of Modern Portfolio Theory is the Benefit of Diversification: the total risk of the portfolio is reduced by holding more securities. In factor investing, there is also a Benefit of Concentration: the total return of the portfolio is increased by holding securities with stronger factors. As you dig deeper to lower-conviction names in the active component of the portfolio, the edge from factor returns becomes dulled.

Active Percentages

To make sure this is kept in context, this is a very basic example using just one factor as the alpha signal. Active quantitative managers have a lot more factors available than just Shareholder Yield and can boost their alpha signal beyond the single factor. But for large cap stocks, Shareholder Yield provides a pretty reasonable expectation on alpha signals: the highest- and lowest-scoring stocks by a factor will have the highest outperformance and lowest underperformance, but as the characteristics degrade the excess returns diminish. Alpha signals are just not as effective as the universe broadens. It is unlikely that a manager has discovered the perfect investment signal separating the universe in half between equal conviction winners from losers. When evaluating a manager’s construction choices, investors should search for conviction around the breadth of their alpha signal.

Go to Part 2 of this article.

  1. Risk-Focused vs. Return-Focused, Risk Controls, and Using Active Share
  2. See Asjylyn Loder’s “Trump Bump Boosts ‘Smart Beta’ Funds” (2/5/17)
  3. Analysis from 1969–2016 (S&P 500 constituents from 1990–2016, largest 500 Compustat companies from 1969–1990).
  4. Large Stocks: U.S. Compustat stocks with a market capitalization greater than average. Analysis from 1982–2016.
  5. As of 12/31/16
  6. U.S. Large Stocks (1969–2016)
  7. See
  8. Russell 1000®</sup> constituents vs. Equal-Weighted (1968–2016)