Quantitative investment managers analyze mountains of data over time to find statistically significant deviations from the norm, and then devise methods to harvest expected returns as asset prices revert to the mean. Over the years, the field has become increasingly narrow and complex, but not necessarily wiser.
Quants’ shortcomings became painfully apparent during the financial crisis. This has driven many investors out of active quantitative strategies and into passive low-cost index strategies. For these reasons, we are at an important juncture in active quantitative investment management; to navigate it well, we must find how best to tap quants’ skills.
We envision a flexible and nimble investment approach, which we believe is more likely deliver performance success over both the intermediate term and the long haul. We see quantitative asset management turning to an eclectic framework that accommodates a wider array of possible outcomes and copes with the frequent occurrence of extreme events (fat tails).
The distinguishing feature of our approach is the recognition that investors must negotiate turbulent periods. Standard active quant models, which rely on static linear methods, may prove inadequate in such markets. We need models that dynamically reflect all portfolio risk exposures, not just those represented by typical conditions.
We urge active quant investment managers to look beyond bottom-up models and incorporate a dynamic, top-down (macro-driven) approach, one with the flexibility to capture shifts in risk and return expectations across an array of asset classes and market environments.
Quant methods can be highly useful when accompanied by qualitative reasoning. Given the extreme events that markets frequently experience, modelers must take seriously their responsibility to engage deeply.