As we have already highlighted in previous articles, factor investing has seen increasing interest from investors, mostly institutional. It is nothing new as investment factors were first identified in the early 20th century. The recent interest for factor investing could be a consequence of the decline in the expected returns from the traditional asset classes, driven by a persistent low growth, low yielding environment in developed economies. Investors believe by investing in factors they obtain more robust portfolios, and enhance returns or reduce risks compare to traditional benchmarks.

For us, fund selectors, it has raised a new challenge in the adoption of factor investing. Now, we must find a way to help investors to choose not necessarily the best active manager in a given asset class, but the best active manager in the risk factors that the asset classes are exposed to.

We would like to discuss in a series of articles those challenges and the approach applied at WSP to address them. To that purpose, we draw our articles essentially from a seminal book containing 16 original articles written by leading industry experts, dealing with key aspects of factor investing (1).

One of the main consequence of the increased interest in factor investing has been the rise of passive investments such as the so-called “smart beta” format. The generic expression “smart beta” may encompasses what is also referred as alternative beta, strategic beta or exotic beta. It offers the opportunity to buy exposure to factors at low-cost, hence pressuring active manager to justify what kind of returns explain an active premium charged to investors.

It is potentially a disruptive force in asset management. It lowers costs for investors while opening access to a range of investment returns. According to authors, smart beta has grown fast over the last 5 years, for equity it is estimated to account for $500 billion to $1 trillion of AUM. They stress that there is a plethora of indices, citing EDHEC, one of the many providers, claiming 3’076 indices on offer.

For fund selectors it implies to move the basis for assessing active managers, by essentially determining to what extent they are offering returns different from factors already available at low costs.

Hence, the fund manager’s ability to deliver returns that are idiosyncratic to the available set of commoditised factors becomes a central measure of active management.

In that context, active management lies essentially in the allocation to those factors in a strategic, dynamic or tactical approach. To notice, this is valid not only for the fund managers to be assessed, but also for the end-investors, depending how the decision is made to allocate to such strategies.

It is a significant change from the univariate benchmark approach made of the traditional cap-weighted benchmarks we used to have, to a multivariate benchmark universe made of smart betas indices.

Three axes of fund activity are needed to analyse active managers: tracking error, active share and idiosyncratic risk. The latter is the share of a fund’s activity that comes from the sources of return not explained by strategic exposure to systematic risk factors. It is measured by running a regression of the fund’s return on the market return and a set of risk factors. It could be expressed as:

  • 1-R2 from the regression, or as the standard deviation of the residuals from such a regression.

The question is what factors we should use as the set of regressors, knowing that the Fama-French factors are not the perfect answer in that situation. As for the authors, we would use the set of factors that are currently available, are liquid and cheap. In doing so it is more a question to position an active fund versus its passive alternatives, rather than to explain its portfolio return.

In the three axes of activity measures, Quant managers usually have low active share, a tracking error likely to vary from low to middling but can achieve idiosyncratic returns.

In the universe of fundamental active funds, the authors distinguish between actual stock pickers, who have a high active share and tracking error and also high idiosyncratic returns, and what they call “emotional stock pickers”, who take exposure to a linear combination of systematic factors, without even realising it.

It is essential to be able to determine how the link between active share, tracking error and idiosyncratic returns works if we want to measure the latter.

Amihud and Goyenkko (2) point out that “selectivity”, equivalent of idiosyncratic risk, and “active share” are similar but not the same. They will differ, for example, if :

  • a fund deviates from its benchmark by taking a position in a different (passive) index. In this case active share would rise, but selectivity would not.
  • Likewise, if a single stock outside the benchmark index is added that has a perfect correlation with the stock it is replacing, then active share will rise, but selectivity will not.

Sapra and Hunjan (3) show that we can separate expected value of the tracking error into a term that is dependent on a sum of systematic risk factors and a term that is dependent on active share and the idiosyncratic risk of stocks.

Where b is the portfolio exposure to systematic risk factors, W is their mutual covariance, AS the portfolio’s active share and σ is the average idiosyncratic stock risk.

Now that we have reviewed how to reward managers only for the part of their return that is unexplained by a simple regression of fund returns on factors, it remains to reward the managers who get the factor call right.

To that end, we may apply the traditional calculation to split out of returns that come from strategic exposure to factors versus those that come from factor timing.

In general, it could be written:

Fund return = stock selection + market exposure + factor/theme exposure + timing ability.

Where the timing ability term is broadly defined as the skill in seeking out market factor or thematic opportunities.

At WSP we understand that the main implication here is that we must dedicate time and resources to identify and monitor the smart beta universe where assets and choice will continue to grow, while costs will fall.

We must be able to translate that into multivariate benchmarks to include in our quantitative analysis process. It improves our identification of truly active managers delivering the alpha that justify management fees superior to that of passive strategies. It also enhances the evaluation of a fund’s risks by comparing it against a broad factor set in addition to the market.

To notice that we consider smart beta as a subset of risk premia strategies in that the latter includes also long/short and cross-asset approaches, in an active or a passive implementation process.

(1) “Factor Investing, From Traditional to Alternative Risk Premia”, 2017, edited by Emmanuel Jurczenko, ISTE Press, Elsevier.
(2) “Mutual fund’s R2 as a predictor of performance”, Review of Financial Studies, vol 26, no3, 2013.
(3) “Active share, tracking error and manager style”, available at: hhtp:// aspx, 2013