In summary, although they are improving and becoming more robust to uncertainty than in the past, almost all quantitative approaches to asset allocation still suffer from various limitations. In a complex adaptive system this seems unavoidable, since their evolutionary processes make accurate forecasting extremely difficult using existing techniques. This argues strongly for averaging the outputs of different methodologies as the best way to make asset allocation decisions in the face of uncertainty. Moreover, these same evolutionary processes can sometimes give rise to substantial asset class over- or undervaluation that is outside the input assumptions used in the asset allocation process. Given this, relatively passive risk management approaches such as diversification and rebalancing occasionally need to be complemented with active hedging measures such as going to cash or buying options. The effective implementation of this process will require not only paying ongoing attention to asset class valuations, but also a shift in focus from external performance metrics to achieving the long-term portfolio return required to reach one’s goals. When your objective is to outperform your peers or an external benchmark, it is tempting to stay too long in overvalued asset classes, as many investors painfully learned in 2001 and again in 2008.
Making It Happen
Using broadly defined asset classes minimizes correlations and creates more robust solutions by reducing the sensitivity of results to deviations from assumptions about future asset class returns, which are the most difficult to forecast.
Equal dollar weighting should be the default asset allocation, as it assumes that all prediction is impossible.
However, there is considerable evidence that the relative riskiness of different asset classes is reasonably stable over time and therefore predictable. This makes it possible to move beyond equal weighting and to use risk budgeting. There is also evidence that different asset classes perform better under different economic conditions, such as high inflation or high uncertainty. This makes it possible to use scenario-based weighting.
Techniques such as mean–variance optimization and stochastic search are more problematic, because they depend on the accurate prediction of future returns. Although new approaches can help to minimize estimation errors, they cannot eliminate them or change the human behavior that gives rise to bubbles and crashes. For that reason, all asset allocation approaches require not only good quantitative analysis, but also good judgment and continued risk monitoring, even after the initial asset allocation plan is implemented.
- Page 5 of 5
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