An Initial Look At Smart Beta And Factor Investing

Since its founding 35 years ago, The Leuthold Group has utilized a distinctive blend of quantitative, fundamental, and technical analysis to guide its investment activities. These disciplines are focused on value recognition, trend analysis, and leadership expectations, all of which influence our portfolio management and tactical-allocation decisions. While quantitative techniques have been around for decades, a recent incarnation of this investment style has taken the industry by storm. Factor investing (also known as smart beta or strategic beta, among other terms) has become the hottest portfolio management trend in the last five years. Morningstar estimates that 700 funds (primarily ETFs) now populate the smart beta space with assets under management exceeding $600 billion.

Smart beta is a generic version of quantitative investing, kith and kin to the disciplines used at firms such as ours. This article is the first in a series we plan to publish on the topics of smart beta and popular quantitative methods. Our motivation is threefold: 1) it’s essential for investors to have a solid grasp of this growing and important industry phenomenon; 2) examining these products may assist us in optimizing our own security selection and tactical tools; and 3) every active manager needs to understand how factors are influencing the structuring, risk positioning, and performance attribution of their portfolios, even if they do not use factors in their decision-making process.

Interest in factor investing has exploded in recent years as investors have searched for a “better way” after the market crash of 2008-09. This discipline traces its roots back to academia, where researchers have spent decades looking for market “anomalies.” Simply put, anomalies are characteristics or traits that appear to cause some assets to provide superior risk-adjusted returns beyond the level expected by the Capital Asset Pricing Model (which holds that excess return can only come from taking more systematic risk). Over the years, researchers have discovered many characteristics that seem to generate excess return without requiring excess risk or superior investment skill. The beginnings of factor investing are often traced back to Fama and French’s 1992 paper which proposed that Value stocks and Small Cap stocks earn unexpected excess returns. Academics have identified hundreds of possible factors since then but, as we shall enumerate later, the industry has latched on to fewer than ten as having true merit.

Factor Research

The academic research that has spawned the smart-beta movement is based on the notion that securities (primarily stocks) have common characteristics/attributes/traits that cause them to reliably produce excess returns. Studies are designed to define such attributes and identify the size and consistency of the excess return. Research generally assigns 1% to 4% per year of excess return to the factor in question, and attractive Sharpe ratios often signify the factor’s superior risk-adjusted return. Based on the notion that a basket of stocks with the positive attribute should be expected to outperform the market going forward, as it has in the past, practitioners build off this research to create a portfolio management process that captures the factor in a methodical and repeatable fashion.

In evaluating which of the scores of factors identified by academic research that are most likely to continue generating excess return, practitioners have zeroed in on a preferred set of criteria to determine the credibility of each candidate.

  • The factor return must be persistent; it should appear in a variety of market environments and recur over time.
  • The factor should be present in multiple countries and sectors. A factor that works in just one market may be the result of an idiosyncrasy particular to that market rather than a true return driver.
  • The factor should work across small changes in definition. A factor that only works using a specific narrow definition is less trustworthy than a factor that is robust across similar definitions of the characteristic.
  • The factor should have an economic or behavioral reason to work.

One of the key analytical questions with any return anomaly is identifying the reason that the factor works. Data mining can uncover all sorts of positive-return correlations (remember the Super Bowl indicator?) but academics and practitioners insist that a factor have some basis for success, providing confidence that the results are not just random noise. One explanation centers on efficient-market theory, proposing that a return factor works because the investor has taken on increased risk to earn the excess return. The other explanation is behavioral in nature, and holds that a factor may deliver excess return because of persistent investor-behavioral errors which cause assets to be routinely over or under priced. Efficient market theory and behavioral errors are both plausible explanations for factor returns and each has the potential to remain viable into the future.

Having identified a believable return driver, one must next determine which stocks have a positive or negative exposure to that factor and o decide how to assemble them into a coherent portfolio. Neither of these is a simple task, and successfully resolving these difficulties reveals why factor investing is much closer to an active endeavor than a passive one.