Kahn and Lemmon (2016) argue that smart beta is a disruptive innovation (in Christensen’s sense) that will fundamentally reshape the active management industry. Published in the Financial Analysts Journal while both authors were at BlackRock.
What is smart beta?
Smart beta strategies are active strategies with some characteristics of passive strategies. They use simple, rules-based, transparent approaches to building portfolios that deliver fairly static exposures to characteristics historically associated with excess risk-adjusted returns — value, momentum, size, quality, and low volatility.
Smart beta is not new in concept. The ideas trace to Ross (1976) arbitrage pricing theory. What is new is the packaging: carving out a significant component of active management and delivering it more cheaply and transparently.
Decomposition of active returns
Kahn and Lemmon decompose any investment product’s expected return into three components:
- Market return — the return to a cap-weighted index benchmark
- Smart beta return — the active return from static exposures to smart beta factors
- Pure alpha — the active return above and beyond static factor exposures (stock-specific selection, macro timing, factor timing)
This decomposition also maps to fees: low for index funds, intermediate for smart beta, high for pure alpha.
The fundamental law framework
From Kahn’s earlier work, expected alpha can be expressed as:
Expected alpha = IC x volatility x score
Where IC (information coefficient) measures forecasting skill, volatility is the cross-sectional dispersion of forecasts, and score is the standardized forecast. Smart beta captures the “common factor” portion of this alpha. Active managers are left competing only on the “stock-specific” portion — which requires genuine skill to deliver.
Empirical evidence of disruption
Kahn and Lemmon examined 79 global equity managers (eVestment, benchmarked to MSCI World or MSCI ACWI, Jan 2010-Dec 2014):
- Time-series regressions of active returns on six Barra GEM3 smart beta factors (market, small size, value, quality, momentum, low volatility)
- Average R-squared: 33% of active variance explained by static smart beta exposures
- For 30% of managers, smart beta explained more than 40% of active variance
- Wide distribution: some managers deliver almost pure alpha, others deliver almost pure smart beta
The fee mismatch
The median active manager charged 65 bps. Multi-factor smart beta ETFs charged roughly 42 bps (average of iShares IEIL at 35 bps and ACWF at 49 bps).
Managers delivering mostly smart beta at active fees face the greatest disruption risk. Their products are effectively overpriced relative to what smart beta ETFs can deliver. The predicted fee for a product should be a linear function of its R-squared: from 65 bps (pure alpha, R-squared = 0) to 42 bps (pure smart beta, R-squared = 1).
Implications for the industry
Active management will evolve into two distinct product types:
- Smart beta products — lower fees, scale advantages, transparent, rules-based
- Pure alpha products — higher fees, requiring strong research capabilities, portfolio engineering to keep factor exposures low
Managers delivering a mix of smart beta and pure alpha face a strategic dilemma: they must compete with smart beta providers on cost and with pure alpha providers on skill. Trying to do both puts them at a disadvantage against focused competitors.
Managers who deliver primarily smart beta at active fees face extinction — it is only a matter of time before clients recognize the fee mismatch.
Why smart beta is different from past fads
Unlike 130/30 strategies or portable alpha, smart beta does not ask investors to drop long-held constraints or adopt unfamiliar risks. It takes ideas already embedded in active products and delivers them more cheaply. The innovation is about cost and packaging, not about doing something fundamentally new.
Three challenges to adoption
-
Performance disappointment — smart beta factors can underperform for 3-5 year periods. Investors may hear more about benefits than risks. The division of responsibility shifts: investors (not managers) choose the factor exposure, so they cannot blame the manager when factors underperform.
-
Education — investors need knowledge and technology to analyze existing factor exposures and build optimal combinations of index, smart beta, and alpha products. This requires evangelism, not just technical papers.
-
Fragmentation — the proliferation of smart beta products and indexes has confused investors. Different definitions of the same factor (e.g., book-to-price vs. earnings-to-price for value) produce performance dispersion. Some traditional active managers oppose the transformation out of self-interest.
Connection to factor investing literature
This paper connects to the broader factor investing framework described in factor-investing-overview. The MSCI foundations paper (Bender et al. 2013) showed that active managers often tilt toward well-known factors and that these tilts explain a substantial portion of returns. Fama and French (2010) found that mutual funds in aggregate underperformed factor benchmarks by roughly the cost of expense ratios. The Two Sigma Factor Lens and Venn platform were built partly for the use case of decomposing manager returns into factor-driven and residual (alpha) components.
Related pages
- factor-investing-overview — comprehensive overview of factor definitions, criteria, and cyclicality
- two-sigma-factor-lens — return-based factor model used for manager evaluation
- return-based-vs-holdings-based-models — how factor models decompose manager returns
- msci-barra-gem — Barra GEM3 factors used in Kahn & Lemmon’s analysis