This blog post is adapted from my French column with the newspaper Les Affaires.

The current trend in the world of investment funds is “smart beta.” The approach meets investors’ growing demand for funds that generate returns above the benchmark indices, without the exorbitant fees generally associated with traditional management involving the analysis of individual securities. In short, smart beta suggests that it can add value even after deduction of costs.

Beta history

Before smart beta, there was beta. Beta was based on a very simple premise worked out by university researchers in the 1960s, namely that the expected return for a given stock depends on the stock’s sensitivity to overall market fluctuations. Beta is a statistical measurement of this sensitivity. For example, if a stock has a beta of 0.50, its expected return is half that of the overall market. Beta is thus an intuitive, elegant mathematical model for estimating stocks’ expected return. Logically, equities with a low beta are low risk, and investors are thus willing to accept receiving a lower return. The opposite applies for high beta stocks: high risk, high return. But there’s a snag: empirical studies show that the model doesn’t work.

Mystery solved

Let’s speed ahead 30 years. In 1992, Professors Eugene Fama (Nobel prize, 2013) and Ken French proposed an answer to the beta conundrum. They suggested that the model didn’t work because, quite simply, it was incomplete. The expected return on equities depends on three factors, not one, namely sensitivity to market volatility, value (preference for value stocks) and size (preference for small capitalization stocks). Eureka! That’s the origin of the Fama and French three-factor model. Although imperfect, it’s the most efficient approach so far for estimating stocks’ expected returns. So there’s not just one beta: there are several, and each of them measures a stock’s sensitivity to a specific factor. This is where the term “smart beta” comes from.

A star is born

When they published their model, Fama and French had spent a decade as consultants for a new portfolio management firm called Dimensional Fund Advisors (DFA). The firm got off to a modest start in 1981, offering passive small-cap equity funds to large institutions. Before long, it added a value stock fund. The extraordinary return potential of small-cap companies and value shares had already been documented by academic researchers well before 1992. Fama and French simply integrated this potential into a single model, sparking interest throughout the world of finance. A real revelation!

Birth of multi-beta, or “multi-factor” funds

Around 2005, DFA came up with the idea of integrating a number of factors into a single fund. The goal was to reduce income tax and transaction costs related to the “migration” problem. For example, when a stock held by a small-cap fund rises in value beyond a certain threshold, it becomes a mid-cap stock and has to be liquidated by the fund and replaced by a new small-cap stock. That generates transaction costs and triggers a taxable capital gain. But integrating all the factors into the same fund made up of large-cap, mid-cap and small-cap stocks means that the “problem” stock doesn’t have to be replaced, thus reducing the volume of necessary transactions.

You may be interested to know that DFA chose Canada as its test bench for the first multi-factor fund, with the launch in 2004 of its Canadian Core Equity Fund.

Factor farmers

Multi-factor funds have contributed to DFA’s impressive growth. Over the past 15 years, there has been about a tenfold increase in the firm’s assets under management. Today, DFA manages assets amounting to at least twice those of the Caisse de dépôt et placement du Québec (CDPQ) or the Canada Pension Plan. DFA has become a Titan. As you can imagine, this hasn’t gone unnoticed by the rest of the financial sector. Academics, consultants, fund management firms: everyone has set out to identify new factors. The financial sector has almost become a factor farm. Professor Campbell Harvey of Duke University has documented hundreds of published factors. OK, he may have exaggerated a bit. In any case, Fama and French involuntarily sparked a new industry, namely factor-based portfolio management. Obviously, it’s improbable that all of the proposed factors are sound. People want to come up with new factors so they can become a professor at a prestigious university or a wealthy consultant or develop a successful new investment fund. If you torture the data long enough, I promise you the figures will tell you everything you want to hear. To apply a factor-based investment approach, you have to separate the wheat from the chaff.

Great! So what’s the answer?

We’ve learned three things so far: (1) We’ve learned what a beta is; (2) There are a number of betas, not just one; (3) We can apply the smart beta investment approach via single-factor or multi-factor funds. Do you want my opinion? First, the only factors I trust are the ones identified or endorsed by Fama and French, namely market, value and size (small-cap stocks), along with another one added in 2010, profitability (I’ll spare you the explanation). Fama and French also acknowledge the existence, at least in theory, of a momentum factor, whereby stocks that have recently outperformed the market tend to keep on rising. However, momentum portfolios have so far generated rather poor results.

As for multi-factor funds, the only ones I’m comfortable with are DFA mutual funds. They are offered only by accredited DFA advisors, including those who work with my employer, PWL Capital. If you are a do-it-yourself investor, I think that certain single-factor exchange traded funds (ETFs) can be a reasonable complement to your total market ETFs and provide you with exposure to market segments that increase your chances of a slightly higher long-term return than a total market index portfolio. A few of these funds are shown in the attached table. As they are U.S.-listed funds, currency conversion can entail costs (sometimes substantial). Find out in advance!


Before you rush to invest in factor ETFs, I advise you to think carefully. This strategy will probably make your portfolio more complicated, and more complicated means a bigger risk of mistakes along the way — for example, aborting your strategy because of unsatisfactory returns over a few years, which can easily happen. Smart beta investing can require the patience of an angel. Generally speaking, I think that most of us can get richer with a “good” strategy that is easy to follow than with an extraordinary strategy requiring iron discipline. Sometimes good enough is really the best answer.

Some single-factor ETFs

ETF Ticker Asset class Management expense ratio Domicile
Vanguard Value VTV U.S. Equity 0.05% U.S.
Vanguard Small-Cap Value VBR U.S. Equity 0.07% U.S.
iShares MSCI EAFE Value EFV International developed equity 0.39% U.S.
iShares MSCI EAFE Small-Cap SCZ International developed equity 0.40% U.S.
Sources: Vanguard, Blackrock