Factor Risk Models Built for Allocators find Statistical Evidence of Skill and Improve Risk Management

New factor risk models, built for allocators with passively-investable factors, distinguish between performance due to security selection and that due to unintended passively-available market exposures* that differ with those of the benchmark.


The difference with conventional factor risk models from Barra and others is that all of our factors are investable as passive ETFs.

From an allocator’s perspective, passively investable factors make are a game-changer.


Passive factor risk models offer robust decomposition of incremental performance into:

  • Passive return: consistent passive exposures that differ from the benchmark (performance available without active fees)
  • Timing return: changes in passive exposures  (part of active contribution, whether or not intentional)
  • Security selection return (worth paying for)


Properly isolating return due to security selection proves to be a breakthrough. We can prove:

  • Past performance is, for the first time, predictive.
  • One-third of active managers take too little active risk to ever compensate for fees — even with skill.
  • Unintended exposures that endanger performance can be cheaply offset.


Detect managers with statistical evidence of skill likely to persist

Managers in the top security-selection skill decile are twice as likely to outperform in the subsequent three years. Managers in the bottom skill decile are more than twice as likely to underperform.


Ensure active managers are taking sufficient security-selection risk to justify active fees

Skill by itself is insufficient. Managers must also take enough security-selection risk to overcome fees.

One-third of active equity mutual funds, and roughly half of fund assets, appear to be closet-indexing, taking too little active risk to overcome active fees, even with top-decile skill.

We require managers with statistical evidence of security-selection skill who also take sufficient security-selection risk.


Know when a manager change is appropriate (and when it is not)

Portfolio performance evaluation is useful only if it can lead to actionable insights. Unfortunately, conventional performance measurement has little to no value, and it is almost never actionable.


It’s well known that past returns are not positive predictors of future returns. In fact, with conventional performance measurement, managers in the top quartile in one period are more likely to be in the bottom quartile the next than to remain in the top.

The problem is that the impact of randomness (which, it turns out, is primarily mean-reverting market exposures that differ from the benchmark) overwhelms any return due to security- selection skill—too much noise to detect a signal. Passive factor risk models distinguish between return due to randomness and return due solely to security selection, isolating the signal from the noise.

Performance evaluation is, for the first time, actionable.


Improve allocations among managers

Asset owners can avoid unintentionally reinforcing passive bets or offsetting active bets among individual managers, offset unintended risk exposures (i.e., risk without expected return), avoid closet indexing the overall portfolio, and better assess how individual managers contribute to aggregate portfolio exposures.

Managers with complementary risk exposures can be combined to reduce risk relative to the benchmark while retaining the security-selection risk worth paying for.


Model Validation

Though mathematically complex, equity risk models are easily tested. Just as we don’t need to understand Google Maps’ time-to-arrival algorithm if we observe that we consistently arrive at the predicted time, we evaluate the accuracy of an equity risk model by comparing returns predicted by past factor exposures to subsequent portfolio performance: We measure factor exposures using end-of-month holdings and predict the following month’s return as a function of index returns.

The correlation between predicted and actual returns measures a model’s accuracy. The higher the correlation, the more effective a model is at hedging, stress testing, and scenario analysis, as well as evaluating investment risk and skill.

Our risk models are highly predictive and deliver over 0.97 median correlation between predicted ex-ante and reported ex-post portfolio returns for both U.S. and Global Equity mutual funds (see: testing predictions of equity risk models and testing global equity risk models).

Skeptical?  We’re happy to provide passive ETF-replicating portfolios for any of your managers, and you can validate our models’ accuracy for yourself by comparing predicted ex-ante with reported ex-post portfolio returns.


Test Drive

Why not take a look at an analysis of one or two of your managers?  We have all historical holdings data preloaded, so there’s nothing required on your part; just reply below with a couple fund names or email michele@peeranalytics.com.





* Exposures to market, sector, size, value, and interest rates (global models add region, country, and currency exposures) are the dollar-weighted sum of individual security market betas, sector betas, etc.


Exposure (beta) examples:

If a fund with a benchmark with market beta of 1.0 has a consistent market beta of 0.8, the fund will underperform by 2.0% when the market is up 10% and outperform by 2.0% when the market is down 10%, absent any impact from security selection.

If a fund has a consistent tech sector beta of 1.5 when its benchmark’s tech beta is 1.0, that fund will outperform by 2.0% if the tech sector outperforms the market by 4.0%

Since market and sector betas other than 1.0 can be obtained passively, performance due to consistent beta differences from the benchmark is not part of active contribution.

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