Between Active and Passive
Traditional index funds weight each stock by its market capitalisation: the bigger the company, the larger the portfolio position. Factor investing — sometimes marketed as "smart beta" — breaks this link. Instead of weighting by size, factor ETFs tilt portfolios toward stocks exhibiting characteristics associated with documented return premiums: small size, low valuation, positive momentum, high profitability, or low volatility.
The appeal is straightforward: capture academically documented return premiums with systematic, rules-based strategies at a fraction of the cost of active management. Factor ETFs are cheaper than active funds but typically more expensive than plain vanilla cap-weighted index funds.
The Main Factors in Practice
The most widely implemented factors in ETF products are:
Value: overweighting stocks with low price-to-book, price-to-earnings, or similar valuation ratios. Examples include Vanguard's value ETFs and the iShares MSCI World Value Factor ETF.
Size: overweighting small-cap stocks. Small-cap index funds are the simplest implementation.
Momentum: overweighting stocks with strong recent price performance, typically rebalancing monthly or quarterly.
Quality/Profitability: overweighting companies with high return on equity, stable earnings, and low leverage.
Low volatility: overweighting stocks with lower historical price volatility, which has paradoxically been associated with above-average risk-adjusted returns.
The Factor Zoo Problem
The proliferation of factor strategies has attracted serious academic scrutiny. Campbell Harvey, Yan Liu, and Heqing Zhu's 2016 paper found that over 300 distinct factors had been published in academic finance literature. With enough data mining — testing hundreds of variables across enough time periods — some factors will appear statistically significant by pure chance.
The concern is not that all factors are spurious but that the bar for publication has historically been too low, and many published factors reflect historical data mining rather than genuine, persistent return premiums. Researchers now argue that newly proposed factors should pass a much higher statistical bar (a t-statistic of 3.0 rather than the traditional 2.0) to be considered credible.
Which Factors Are Most Credible?
The most theoretically grounded factors are the original Fama-French ones: the market premium, the size premium, and the value premium. These have decades of out-of-sample evidence (including in international markets), theoretical rationale (risk-based or behavioural), and economic intuition behind them. Momentum is also very well documented internationally, though its lack of a clean risk-based explanation makes some researchers cautious.
The proliferation of niche factors — "quality at a reasonable price," "earnings revision momentum," "accruals anomaly" — is more suspect. Many have performed poorly once launched as investable products, consistent with the theory that publication and widespread trading arbitrages away the premium.
Factor Timing: An Additional Layer of Difficulty
Some practitioners attempt to time factor exposures, rotating toward value when it is cheap relative to growth, or reducing momentum when crash risk appears elevated. The evidence for reliable factor timing is thin. Adding a timing overlay introduces another source of potential underperformance on top of the factor selection decision — compounding the difficulty of beating a simple cap-weighted index.