Commentary: How factor analysis sharpens investors’ evaluations of managers

Picking the right investment managers is a central challenge for any institutional investment program. The selection process can be complex, encompassing evaluating everything from track records to operational and personnel practices.

Criteria widely used to evaluate managers are known as the P's: people, process, parent, partnership, philosophy, price, and/or performance. While some are expressed numerically, most are qualitative in nature. Much of the information on topics such as culture, adherence to strategy, risk management and alignment of interests is typically gathered via questionnaires and manager interviews — in other words, qualitatively.

These questionnaires and manager interviews often can be supplemented with a variety of quantitative insights. And now, following the shift to virtual meetings from on-site visits, less time spent traveling has opened up the opportunity for more information gathering and quantitative analysis before meetings with managers.

That quantitative analysis starts with the most quantitative of the P's: performance. When evaluating an active manager, it's easy to look at past returns on both an absolute basis and relative to benchmarks and peers. But the results of those analyses won't explain how the manager achieved those returns. It is through factor analysis that investors will find answers to a series of critical questions and determine whether managers are meeting expectations and delivering what they claim.

Looking at the raw historical performance of a manager is a good starting point, but it tells an incomplete story. A potential investor may want to know how that manager generated the returns they did and what risks they were exposed to.

Factor analysis is designed to help an investor understand the underlying sources of risk and return in a manager's portfolio. By regressing a manager's time series of returns on a set of known risk factors, an investor can quantitatively understand the manager's average sensitivities to those risk factors and how they contributed to the manager's volatility and returns over its history.

Another key output of this factor analysis is the amount of risk and return that cannot be explained by the manager's factor exposures. This component, known as the residual, represents the unique, idiosyncratic risk the manager delivered above and beyond the factors used in the analysis. This risk may or may not have been compensated over the manager's history, and factor analysis will indicate whether the residual return has been positive or negative. A positive residual return is often referred to as "alpha," or true manager skill, however it could also be explained by something else (such as factors not used in the analysis, differences in factor definitions, etc.).

Accessing this quantitative risk analysis can enhance qualitative assessments because it arms the investor with more detailed information that can lead to better questions and dialogue with a prospective manager.

While factor analysis can deconstruct a manager's risk and returns into factors and residual across its history, another pivotal question is how those factor exposures might have shifted over time. Applying factor analysis across rolling time periods can reveal interesting trends in a manager's factor exposures.

For example, a manager may have averaged a certain factor exposure over its entire history, yet that exposure may have fluctuated, especially during stretches of market turbulence. Scrutinizing a manager's equity beta in early 2020, for example, may indicate whether a manager was more exposed to global equity market risk than usual going into the COVID-19 market crisis. And digging into the manager's exposure to "crowding" risk might have identified whether the manager was overexposed to stocks that fell prey to short-selling squeezes early in 2021.

Changes in factor exposures could be very telling, especially when analyzed in the context of the manager's mandate. Changes could indicate style drift for managers that seek to provide consistent factor exposures. Alternatively, it could verify the strategy of another manager whose mandate is to provide tactical and/or style-agnostic exposures over time.

In addition to factor exposures, an investor can also quantitatively judge how a manager's factor and idiosyncratic risks have changed relative to each other, rising or ebbing in importance over time.

As Citigroup observed in a report last year, "leading asset owners have enhanced their ability to identify and measure idiosyncratic as opposed to factor-driven returns and are shifting their allocation approach to emphasize the former." Factor analysis can reveal not only whether a manager has delivered positive residual returns historically, but also whether those residual returns have been consistent.

Rolling factor return attribution can uncover deterioration in a manager's ability to produce returns that surpass what can be earned by exposures to known risk factors. Any decay in the residual return might be something worth digging into to understand how a manager expects to turn it around, especially if the investor is seeking an uncorrelated return source to diversify their portfolio.

Factor analysis can be an effective tool in a quantitative research kit when performing due diligence on prospective managers. It goes beyond surface-level performance metrics to the underlying sources of risk and return.

This just scratches the surface of the insights that factor analysis can unlock. Breaking down a manager's risk and return into their underlying drivers can open other useful lines of quantitative inquiry, such as drawdown simulations, estimates of forward-looking performance during market shocks and regime analysis.

More importantly, factor analysis empowers the investor to ask questions that otherwise wouldn't have been asked and to identify gaps between what's shared in a marketing deck and risk exposures that are evident in past returns. Such insights can greatly enhance an investor's overall picture of a prospective manager when paired with the qualitative side of their assessment process.

Alex Botte is a vice president on the client portfolio management team at Two Sigma Advisers LLC and Two Sigma's investment and portfolio analytics platform Venn. She is based in New York. This content represents the views of the author. It was submitted and edited under P&I guidelines but is not a product of P&I's editorial team.