Investors are often their own worst enemy, buying high and selling low, when sticking to a long-term strategy would serve them best. Many studies document this fact, finding that individual investors routinely underperform the mutual funds they invest in through poor timing of their transactions. This difference, or gap, is caused solely by investor behavior, what we refer to as the Behavior Gap.

To counteract this Behavior Gap and help our clients take advantage of market returns, and other investors poor behavior, our Adaptive Allocation Strategies combine principles from the efficient market hypothesis (EMH)[i] and behavioral economics[ii] to create robust, low cost, tax-efficient, risk-managed portfolios.

We start by building globally diversified portfolios to eliminate unnecessary risks and design an appropriate asset allocation to minimize unneeded risk and maximize the likelihood of achieving our clients’ goals.  This has its roots in the efficient market hypothesis which tells us that an investor who wants higher expected returns must accept more risk.  While this is true, EMH also assumes that assets will always act in accordance with their long-term average behavior. That is, that markets will deliver steady returns with stable risk and exhibit consistent relationships with one another. However, a simple observation of asset class behavior throughout history quickly dispels this illusion. Given that prevailing market conditions can significantly alter risk/reward ratios among asset classes, we reach two primary conclusions:

• To the extent that a relation between risk and reward exists, it is unlikely to be stable over time.
• As the risk/reward relation varies, it is necessary to adapt to changing market conditions to better achieve a consistent level of expected returns.

Adaptive

To account for this variable relationship between risk and reward, our adaptive approach draws on research from Nobel Prize winner, William F. Sharpe[iii], and others that shows the source of risk and degree of risk embedded in various asset classes change over the course of the business cycle. As an example, stocks are riskier later in the business cycle than they are at the beginning.  To maintain a more consistent level of risk, we adapt our portfolios as market conditions and associated risks change.

Evidence-Based

The process of buying low is hard; if it weren’t, the Behavior Gap wouldn’t exist.  Our rules-based investment process systematically incorporates evidence-based behavioral and market factors that research has shown to be the largest contributors to long-term alpha. Then we regularly reallocate client portfolios to purchase assets with higher expected returns while maintaining your portfolio diversification and risk target.

Risk-Managed

Finally, we implement Guardrails, our proprietary rules-based risk-management system, to reduce volatility and drawdown within our portfolios and improve the likelihood that our clients can remain invested through the bear markets that will inevitably come.  Even the greatest portfolio or strategy in the world is useless if investors can’t stick with it.

Our research shows that adaptive portfolio management can be a valuable tool for limiting drawdowns during periods of high volatility and dynamically adjusting portfolio positioning towards asset classes with favorable risk, return and correlation characteristics.

This hypothetical illustration shows how a moderate client portfolio could adapt to changes in the market at the broadest level, as the risk/reward relationship changes.

If you have questions about your portfolio diversification, potential downside, or how you can avoid the Behavior Gap and more consistently grow your wealth, let us know.  We look forward to talking with you.

Footnotes:

[i] A market theory that evolved from a 1960’s Ph.D. dissertation by Eugene Fama.

[ii] Behavioral economics studies the effects of psychological, social, cognitive, and emotional factors on the economic decisions of individuals. This is contrast with EMH, which assumes that people are calculating, unemotional maximizers.

[iii] Sharpe, William F. “Adaptive Asset Allocation Policies.” Financial Analysts Journal 66, no. 3 (May/June 2010): 45-59.