We believe that in addition to diversification across broad asset classes, avoiding large downside losses is also critical to achieving long term investing goals.
Hence, our focus is on identifying asset classes that are dangerously overvalued today, and, at a longer-term horizon, identifying emerging threats that could cause substantial changes in uncertainty and asset class valuations.
Our research is based on the application of complex adaptive systems theory and advanced forecasting methods to macro factors such as technological, economic, environmental, military, social, demographic, and political trends and uncertainties.
We believe that financial markets are filled with positive feedback loops that produce nonlinear effects through the interaction of competing strategies (for example, value, momentum, and passive approaches) and underlying decisions made by people with imperfect information and limited cognitive capacities who are often pressed for time, affected by emotions, and subject to the influence of other people.
While attracted to equilibrium, we believe that financial markets are best described as adaptive systems that never reach it. When they are operating far from equilibrium, substantial over and undervaluations are the usual result.
In contrast, traditional mean-variance optimization is based on an underlying assumption that markets generally operate in or close to equilibrium. This is why this approach often produces disappointing portfolio results.
Our benchmark model portfolio is equally allocated between broad asset classes in order to capture the underlying system and social drivers of financial market returns.
To achieve long-term portfolio goals, avoiding the large losses that follow substantial overvaluations is critical.
This can be achieved through a combination of systematic and episodic portfolio rebalancing that is driven by the extent of asset class over and undervaluation.
In a nutshell, the active investor believes that he or she can regularly generate (or choose fund managers who can generate) returns that are above the returns generated by a passive benchmark portfolio, due to a mix of information and/or forecasting skill (i.e., an "edge") that is superior to that possessed by other active investors.
In contrast, the passive investor wants only to match those benchmark returns at the lowest possible cost.