Factor Investing Mastery in Under 6 Minutes

Ryan Poirier, ASA, CFA, FRM | Director, Index and Product Research | ryan@saltfinancial.com

  • Factor portfolios are built by having positive (negative) exposure to securities with positive (negative) expected excess return, based on past data.
  • Factor-based analysis can help simply break down returns for any equity portfolio into contributions from multiple factors.
  • Like all models, factors are not perfect but provide some important context for investors to better understand drivers of past returns and estimates for the future.

“Essentially, all models are wrong, but some are useful.” – George Box

Factor investing has increased in popularity in recent years, eclipsing the poorly named “smart beta” as a rules-based alternative to active stock-picking for investors. The style has its roots in academia, which benefits from many years of analysis by highly educated people but also the drawback of making the average professional feeling lost trying to keep up with the jargon. But when broken down, the main idea behind factor investing is relatively simple and can be explained in plain English.

A factor is simply an attribute of a stock. For example, a factor could be the company’s market capitalization (the well-known size factor) or something nonsensical like the length of the company’s name. Both describe the company in some dimension. But one describes a real economic risk factor that can be shown to deliver higher returns based on estimates from past data whereas the other is arbitrary and highly unlikely to explain much of anything [1].

While this may sound complicated, the basic steps are straightforward. To form a factor portfolio, the universe of stocks is separated into (for lack of better words) good, neutral, and bad groupings based on the attribute. The factor portfolio is constructed by being positively exposed to the good group and negatively exposed to the bad group, thereby isolating the attribute in question to determine how they might explain a stream of investment returns. This can be implemented in several ways. One is to simply select stocks that rank highly in that factor (the good group) and hold them in a long-only portfolio, eschewing exposure to the neutral or bad group. The other involves holding long positions in the good group while keeping short positions in the bad group, creating a market-neutral portfolio with “pure” exposure to the factor.

The long-short combinations of good and bad attributes underlying the factors have a particular use – explaining the past returns of nearly any equity portfolio. For example, isolating the return of a portfolio that is long small stocks and short large stocks allows you to measure how much of a fund’s return comes from positioning in smaller stocks. Other factors include value (usually expressed by low price-to-book ratios), momentum (the tendency for recent winners to continue winning and vice versa with losers), or quality (usually some combination of profitability/low leverage/high return on equity). By creating long-short baskets of stocks ranked by these attributes, it is possible to decompose a series of returns into contributions from each factor. This can be useful in analyzing the returns from index-based products such as factor-based ETFs. But it can be equally useful to peer into the drivers of return for actively managed funds as well.

As of July 31st, 2018, the Fidelity Contrafund had over $130 billion dollars in assets under management and boasts nearly a 13% compounded annual return since 1990, besting the S&P 500 by over 300 bps per year [2]. It’s a popular fund with good long-term performance, run by the same portfolio manager, Will Danoff, since 1990. As of today, it is the largest active fund managed by a single individual (most large asset managers use a team approach to portfolio management). Using factor-based analysis, we can break down the returns of the Contrafund to determine the drivers of performance over Mr. Danoff’s entire run since 1990.

Exhibit 1: Fidelity Contrafund’s Average Monthly Excess Return Decomposed into Average Monthly Contribution to Excess Return for Each Factor

Factor-Investing

Source: Ken French’s Data Library, Salt Calculations. Data from December 31, 1989 to July 31, 2018.

On the left-hand side is the average monthly return less the risk-free rate for the Contrafund of 0.89% since 1990. We use the three-factor model (introduced in 1992 by Eugene Fama and Ken French) to decompose this 0.89% monthly return into several components. The three-factor model adds size (small minus big) and value (high book to price [inverse of price to book] minus low B/P) as factors to the market exposure (known as beta, from the Capital Asset Pricing Model [CAPM]).

The market return—beta—is generally the largest contributor to return in any diversified equity portfolio, which is intuitive. The market portfolio generated an average excess return of 0.66% over the same period with the Contrafund capturing 82% (the factor “loading”) of this return to arrive at a 54 basis point contribution. The size and value factors both generated 0.18% per month in return, but the loading for each in the Contrafund was negligible, resulting in close to a zero contribution to the return (if anything, the negative loading to value suggests a slight tilt towards growth stocks). The remainder of 0.35% per month in this framework is attributed to random noise. More precisely, it accounts for attributes that are not explained by this particular model.

Given that factors are rooted in academic finance, investors and advisors should understand the process and limitations and not get caught up in the jargon. The goal is simply expressing investment returns as a combination of different factor portfolios’ returns. Given the risk factor, these portfolios are built with long exposure to the securities that are believed to deliver superior forward returns and short exposure to the inferior ones. The result is a powerful tool that can be used to both construct portfolios that target specific attributes while also serving as a measuring stick to better understand how factors contribute to equity returns over time. No model is perfect, but as approximations of reality they can help investors make better decisions in the face of an uncertain future.

[1] Economic risk factors in this context are those which are widely accepted in the financial literature. These are factors such as size, value, momentum, quality, liquidity, volatility, etc.

[2] Source: Bloomberg


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