EAM – The Blueprint for Rescuing Active Investment Management

FULL PAPER IN ABOVE LINK

There have been innumerable papers written in recent years explaining why passive management is the heir
apparent to traditional active management. This is not such a paper. Nor is this a paper urging patience because
active management is on the cusp of reclaiming dominance. Without structural change, it cannot. In fact, pivotal
data presented in this paper quantifies that evolutionary improvements will be insufficient to alter active
management’s inferior position versus passive. Status quo has become a permanent trap for active managers.

Fortunately, a structural solution exists, and it is based on translating proven best practices for predictive analytics
from other industries, to that of investments. This approach, known as Ensemble Active Management (“EAM”),
has the capacity to generate enough added alpha to allow active management to reclaim dominance in its ongoing
battle with passive investing. The improvement is significant enough, and differentiated enough, to arguably be
considered a third category of investing: Passive, Traditional Active, and now Ensemble Active.

Active managers are inherently in the prediction business. This is not referring to ‘market timing’, but rather
predicting or forecasting – based on research, analytics, experience, and skill – which stocks are most likely to
outperform. Other industries have for decades been achieving substantive leaps in predictive accuracy, including
weather forecasting, voice and facial recognition, medical diagnostics, car navigation (e.g., Google Maps), credit
scoring, and virtually all ‘big data’ analyses. It is long‐overdue for these best practices to be embraced by the
investment industry.

EAM is not theory – it has been in live operation for two years, and EAM Portfolios are now commercially available
to the public. As will be shown, a critical component of the validation of EAM is live market performance.
EAM is also not a simplistic “AI” alternative to traditional stock picking. EAM does not discard investment
professionals in favor of machines. EAM builds upon proven investment concepts and established techniques, and
then enhances them through application of modern predictive analytics.

Finally, EAM can operate at massive scale, holds the real potential to persistently outperform passive investing, and
is therefore a valid, viable, and achievable blueprint for retooling the existing engines of active management.

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