A New Perspective
Over a third of active U.S. equity mutual funds are currently so passive that, even if they exceed the information ratios of 90% of their peers, they will still fail to overcome a typical fee.
New passive factor risk models distinguish between performance due to security selection and that due to unintended passively-available market exposures that differ with those of the benchmark, detect security-selection skill likely to persist, and improve risk management.
Active contribution is not simply performance relative to a particular benchmark, it is instead only that portion of incremental return that could not have been obtained passively. The true passive alternative to an active portfolio is rarely a single index, but rather the combination of indexes that replicate the portfolio’s market, sector, and style betas.
Active skill exists …. but it is challenging to detect reliably and, even when present, decays over time.
Since factor noise dominates nominal returns, the use of nominal returns to detect evidence of investment skill takes far too long to be practical. But by distilling stock-picking performance (alphas, residual returns) from factor noise, statistically significant evidence of investment skill can become evident in months, rather than in decades.
Beta differences with a benchmark are a byproduct, usually unintentional, of any stock-selection process. Since properly identified market, sector, and style beta-differences can be freely obtained or offset, they should not be considered part of active contribution.
The conventional approach to evaluating past performance does not tell us anything useful about the future. Portfolio performance evaluation is useful only if it can lead to actionable insights.
Security selection returns, when properly calculated with a robust factor model, persist and yield portfolios that outperform. Both skill and lack of skill persist, and the lack of skill persists most strongly; while it is important that investors correctly identify the talented managers, it is even more important to divest from their opposites.
Whereas nominal returns and related simplistic metrics of investment skill revert, security selection performance – once properly distilled with a capable factor model – persists.
Domestic fund with high security-selection skill, despite significantly underperforming its benchmark
How much downside can your business tolerate?
Presentation summarizing the value of using an equity risk model built for oversight
For Many Equity Funds, Bond Risk is More Important than Industry and Style
Or How to Tell if You Are Paying Top Dollar for a Flawed System.
Insights from isolating active contribution from passively-investable exposures.
Returns-based analysis can be effective—but only when a manager does not significantly vary exposures to passive market, sector, and macroeconomic factors.
Security selection returns of the top U.S. stock pickers in 2016 were positive. When hedged to match market risk, a consensus portfolio of the top institutional U.S. stock pickers outperformed the Market by approximately 2%.
20% of long U.S. hedge fund portfolios surveyed are currently so passive that, even after exceeding the information ratios of 90% of their peers, they will still fail to merit a typical fee. Investors must monitor the evolution of their hedge fund managers towards closet indexing and mitigate fee harvesting.
ESG constraints can create unintentional systematic exposures within equity portfolios. Once identified and measured, these exposures are easily managed.
Properly-designed risk models can be used to filter out the effects of systematic risk, exotic market bets, and luck. When these models are designed from the ground-up to evaluate skill, and are combined with robust statistical techniques, the result is predictive analytics.
Traditional, or dumb, Market and Sector Betas account for over 92% of variance for most U.S. equity smart beta ETFs. Smart beta, unexplained by the traditional Market and Sector Betas, accounts for under 8% of variance for most U.S. equity smart beta ETFs. With proper analytics, investors and allocators can guard against elaborate re-packaging of dumb beta as smart beta.
Active Share is a poor proxy for active risk, with predictive power only ten to fifteen percent of that achieved with robust equity risk models.
Hedge fund survivor bias is especially insidious for the largest firms. Large hedge fund survivor bias overstates expected performance of the biggest firms by nearly half and their risk-adjusted return from security selection (stock picking) by 80%. It is impossible to predict the largest funds of the future, but one doesn’t have to – robust skill analytics identify funds that will do even better in the future than tomorrow’s largest.
Property and casualty insurance company portfolios share a few systematic bets. These crowded bets are the main sources of the industry’s and many individual insurers’ relative investment performance. Since the end of 2013, these exposures have cost the industry billions.
Equity portfolio oversight that fails to consider market and other factor exposures overlooks the principal drivers of performance and risks focusing on noise.
We analyze historical positions and returns of approximately 3,000 non-index U.S. Equity Mutual Funds over 10 years.
Passive exposures to market and other factors overwhelm the impact from security selection and market timing on risk and return – explaining 98.7 percent of absolute performance and 2/3 of performance relative to the market.
Complex equity risk models may offer no better predictions than robust statistical models with a few intuitive factors.
An intuitive Global Statistical Equity Risk Model using Regional and Sector/Industry factors delivers over 0.96 correlation between predicted and reported portfolio returns for a median U.S. Equity Mutual Fund.
Academic analysis favors factors with less explanatory power than industry’s real-world modeling; the explanatory power of sectors is slightly higher than that of size, and approximately four times greater than that of value/growth.
Effective benchmarking provides sufficient information to determine as early as possible when corrective action is necessary.
Plagued by overfitting and collinearity, returns-based style analysis frequently fails, confusing noise with portfolio risk.
With predictive analytics and a robust model, investors can not only identify persistently strong stock pickers but also construct portfolios with predictably strong nominal performance.
What would you do if your equity manager underperformed the index by six percent? Three steps to meaningful, actionable performance reviews.
Properly isolating active contribution from passively-available exposures reveals true active risk, persistent security-selection skill, and unintentional risk exposures or gaps.