When you ask investment professionals what they mean by “alpha,” they typically mean excess return.  This is the definition it was given back when the capital asset pricing model (CAPM) was developed.

In an efficient market, the expected value of the alpha coefficient is zero.  This definition makes a large assumption, however: that all investors have the same “utility.”  Utility is an abstract concept that comes from the Utilitarian school of economic thought that assumes individuals all share a common measure of satisfaction.  Preferences, in this case, are not unique.

By this theory, there is also a single pool of alpha, making alpha extraction a zero-sum game: one trading party wins and the other loses.

We strictly believe, however, that preferences are highly unique to the individual investor.  If preferences are highly unique, then, how can we re-address what “alpha” is?  Consider the following graph, which demonstrates an investment and a strategy tracking that investment:

draw-down-avoidance

At a certain point, the strategy exits the investment, protects against drawdown, and then re-enters the investment.  At the end of the day, while realized volatility is much, much lower, there hasn’t been any excess return generated.  So is this an alpha-neutral trade?  Frankly, the amount of money the investor saved on heart-burn medication and therapy during the drawdown could probably be considered a financial win, but the emotional duress avoided is an intangible alpha that is very real to a client.

There is a second view, however, that demonstrates that the alpha in this situation can be very real.  Consider the case that this strategy is in play for a retirement account that has fixed withdrawals.  Unfortunately, this withdrawal falls almost exactly at the deepest part of the drawdown, locking in losses.

This second graph is for two client accounts with such withdrawals: one that just tracks the investment and the other that tracks the strategy.

draw-down-avoidance-portfolio

We know from the first graph that the strategy didn’t actually create excess return; but from the point of view of the client, alpha was actually generated.  Unfortunately, this alpha will never show up on client statements: it only shows up if you compare the value of action versus non-action in retrospect.

Roger Nusbaum, on his blog Random Roger, wrote an article entitled: "'Beating the Market' Makes for an Incomplete Discussion".  In the post, Roger discusses passive versus active management and the role of active management beyond alpha creation.  Said another way, "alpha should be part of the discussion, but not the whole discussion."  Wealth management requires a holistic view of a client's financial situation: there is no single solution or strategy that fits all clients.  That's why at Newfound we create products driven by rules: we believe the rules create increased transparency and consistency in product behavior, allowing clients to more easily take a "whole portfolio view."

By singularly focusing on the precise, mathematical definition of alpha, we forget the reason why we may have included a particular active or passive exposure in our portfolio.  As quants, instead of seeing the future as a single event, we view the future as a distribution of possibilities: some events more likely and some less, but never predictable.  Therefore, we believe that portfolios should be designed that will be robust as possible to these infinite possible futures.  Ultimately, this may mean that for the single path that is realized our portfolio "underperforms," but the measurement fails to capture that for many of the other infinite paths, it outperformed.  Once we take a probabilistic view of the future, the precise question of "did I create alpha over this particular event horizon?" falls short of a much more important question: "did I achieve my wealth goals and would I have if things turned out differently?"

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients.

Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn.

Corey is a frequent speaker on industry panels and contributes to ETF.com, ETF Trends, and Forbes.com’s Great Speculations blog. He was named a 2014 ETF All Star by ETF.com.

Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University.

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