ESG constraints can create unintentional systematic exposures within equity portfolios. Once identified and measured, these exposures are easily managed.

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.

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.

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.

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.

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.

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.

Tax considerations influence both relative rates of expected return and variability of asset classes, increasing the relative attractiveness of equity.