Beta differences from a benchmark are a byproduct, usually unintentional, of any stock-selection process. Since properly identified beta-differences can be freely obtained or offset, they should not be considered part of active contribution.
Domestic fund with high security selection skill, despite significantly underperforming its benchmark
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.
Over a third of 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.
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 separating active contribution from differences in passively-available 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, 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.
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%.
Whereas nominal returns and related simplistic metrics of investment skill revert, security selection performance – once properly distilled with a capable factor model – persists.
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.
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.
Equity portfolio oversight that fails to consider market and other factor exposures overlooks the principal drivers of performance and risks focusing on noise.
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 two-thirds of performance relative to the market.
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.
And The Flaws of Blind Fund-Following Strategies
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.
Presentation summarizing the value of using an equity risk model built for oversight
Avoid complexity for the sake of complexity.
We analyze historical positions and returns of approximately 3,000 non-index U.S. Equity Mutual Funds over 10 years.
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.
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.
Impact of Taxes on Asset Allocation
Tax considerations influence both relative rates of return and variability of asset classes, increasing the relative attractiveness of equity.