Factor indexes: Choose with care

Recently there has been a proliferation of investment products focused on factor-based investing, including factor-based index funds, single-factor portfolios and so-called smart beta. Common features include investment theses tied to well-known return anomalies, rules-based portfolio composition and systematic implementation. However, what many investors might not know is that despite a strong marketing push by a number of firms that label this as a “new” and “innovative” investment approach, these types of strategies are in fact not new — they have actually been around for decades. And, if not utilized correctly, they can carry significant risks.

Single-factor approaches can offer utility in many types of situations — for example, as completion funds, to plug a temporary exposure gap, in a transition or as a strategy benchmark. However, many of these new products on the market are highly simplistic, packaged for high volume and ease of implementation. Essentially they are assembly-line products, proliferating at an astonishing rate. In fact, EDHEC's Scientific Beta institute offers more than 4,200 smart beta indexes. This focus on scale means that such products dispense with valuable attributes of more sophisticated factor-based approaches. Investors should be aware of what they could be leaving on the table.

Factor-based investing is the systematic application of predictive ideas to a broad investment universe. Academic research has long demonstrated the existence of persistent anomalies linked to excess return. These are caused by common behavioral errors made by investors and also by the structural constraints in markets and organizations that limit the full implementation of information.

Investors should be attuned to the fact that many of the factor-based products on the market today are designed not around the optimal exposure to an anomaly, but rather the optimal fund structure for mass distribution potential. This puts a premium on qualities like simplicity of investment thesis, process transparency, and the liquidity of holdings. Such products are often accompanied by costs and risks that are difficult for investors to assess.

Factor-based index funds, for example, are often marketed based on an historical simulation showing the performance of the given attribute relative to the market over time. It is rare to see a back test that accurately reflects the cost of maintaining exposure to the anomaly in a real world portfolio. Factors are moving targets, and the group of stocks that provides your exposure today might not have the same characteristics a month from now. A manager thus has a choice — either to chase the factor rigorously while piling up transaction costs, or to go more slowly and accept a constant lag in factor exposure.

A more robust quantitative investment framework is focused on investor outcome, not on simply providing the promise of exposure to a given theme. One approach is to use alpha modeling to identify the stock characteristics or combinations of characteristics associated with high risk-adjusted returns, then optimally composing a portfolio by simultaneously considering return forecasts, risk and costs. It's also essential to stay highly focused on implementation — trading a strategy to gain precise exposures while minimizing transaction costs and unintended risk. This type of approach can provide investors the opportunity to gain exposure to attractive return factors without trading those exposures for unintended risks or costs. Expensive valuations, for example, are a very real risk in a portfolio focused solely on quality. More sophisticated approaches can deliver a factor exposure without undesirable side effects — or at least be able to tell investors what these are likely to be. And rather than simply providing factor exposure in an index-like manner, they can actively seek alpha within the factor portfolio.

In short, while simplistic factor-based products share the broad goal of identifying and systematically harvesting return premiums at low cost, naive versions employ investment processes that are more like factor factories. They have a quant-like approach, but are so constrained by marketing-related requirements that many of the benefits of a structured approach are dispensed with. These restrictions might result in lost alpha, unintended risk exposures and suboptimal implementations. Moving forward, simplistic factor portfolios trivialize the importance of ongoing research to better understand the source of mispricings and the best ways to capture them in a changing marketplace.

Whether in a single or multivariate factor strategy, a true quantitative active approach from a manager that seeks alpha, owns their track record and is not just going to invent another index if the current one fails to sell will prevail. Monetizing factors in simplistic implementations is not the same as true quantitative investing, and investors should be aware of the difference.

John Chisholm is executive vice president and chief investment officer of Acadian Asset Management LLC, Boston.