The Three Variables That Drive Returns In Factor-Based Portfolios – ValueWalk Premium
Factor-Based Portfolios

The Three Variables That Drive Returns In Factor-Based Portfolios

Factors due diligence from the head of Vanguard’s factor ETFs

As factor-based investing becomes a bigger part of the product universe, it’s worth taking a few moments to understand how quant managers and index providers go about achieving factor exposure. Having these insights can help you understand that different providers do it differently which, in turn, can help you give clients better advice related to implementing factor strategies.

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Q2 hedge fund letters, conference, scoops etc

To begin, factors are the underlying drivers that influence and explain the way an investment behaves. A portfolio that exhibits a high sensitivity to the movements of a particular factor within the overall market is considered a “factor portfolio” because its returns are primarily driven by the corresponding factor’s movements. The goal of such a portfolio is to exhibit a high correlation with the targeted factor’s behavior, so that investors can achieve exposure to a desired factor efficiently.

As we construct factor driven portfolios the question we have to ask is: What makes a portfolio sensitive to a particular factor? Three main choices affect this sensitivity: stock selection, stock weighting, and implementation.

It starts with stock selection

A portfolio will have high sensitivity to a particular factor if the stocks within the portfolio have similar attributes to the ones used to define the factor. For example, if the value factor is a proxy for cheaper stocks—typically defined by low price-to-book ratios relative to the broad market—then a value factor portfolio would likely favor low price-to-book stocks in its construction methodology as well.

There is a large debate within the asset management and index industries around which characteristics should be used to target a given factor in a portfolio.

Generally, the less the academic research supporting the existence of a factor, the larger the disagreement about how to capture it.

For more established factors, such as value, most of the industry has converged toward one characteristic or a combination of price to book, price to earnings, price to cash-flows, and price to sales. For less-established factors, such as quality or liquidity, there is considerably less agreement and thus it’s possible to find very different characteristics across different managers or index providers.

Three is the magic number

Another topic of debate is how many characteristics managers or index providers should use to adequately capture factor exposure. Although some managers have taken a purist approach to factor investing, using just one characteristic, most of the industry uses three. There are advantages and disadvantages in combining multiple characteristics.

On the one hand, using multiple characteristics yields more robust portfolios, reducing individual variable noise, data mining concerns, and potential crowding effects. On the other hand, combining too many variables leads to higher complexity, increasing portfolio-monitoring costs and due diligence efforts.

At Vanguard, we believe that using three broad, underlying characteristics to construct individual factor products strikes the right balance between robustness and complexity. Our goal is to be as transparent as possible and to use variables that are recognizable and familiar to investors.

That said, in the case of factors whose measure may be more multifaceted, such as quality, we may seek to use more than three characteristics to capture the many different aspects of a company’s quality.

Overall, we have chosen variables that are well documented in the empirical literature. However, we want to be deliberate and avoid using methods that might lead to an excessive concentration in a given sector. Also, we want to incorporate variables that exhibit different behaviors across the business cycle.

Adjusting stock weighting

Selecting stocks with similar attributes is not enough to have high sensitivity to a particular factor. Another important choice is the methodology used to weight stocks within a portfolio. Given a set of characteristics, a portfolio that gives higher weight to the securities with the strongest factor attributes is likely to have higher factor sensitivity.

With our single-factor products, we rank each stock within the universe across each of the underlying characteristics for a given factor. Next, we compile an equally weighted “composite ranking” for each stock, and then convert that ranking into a factor score. We then construct our portfolio by including the highest-scored stocks until we have reached one third of the market capitalization of the starting universe by weighting each included stock according to its factor score.

Factor-Based Portfolios

Read the full article here by Antonio Picca of Vanguard, Advisor Perspectives


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