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Risk Control through Dynamic Core-Satellite Portfolios of ETFs: Applications to Absolute Return Funds and Tactical Asset Allocation — January 2010

3. Beyond Tactical Bets: Integrating Predictions in a Risk-Controlled Framework

Hit ratio

7/12 5.68%

8/12 8.01%

9/12 10.48%

10/12 12.91%

11/12 15.49%

Average expected return

Average maximum drawdown

-13.24%

-10.57%

-8.49%

-6.66%

-4.52%

Worst maximum drawdown

-28.09%

-25.21%

-22.92%

-19.03%

-16.17%

Worst performance over a rolling one-year period

-22.91%

-22.07%

-18.38%

-15.76%

-12.55%

Exhibit 5: Forecast-based standard tactical allocation: this table shows results for the tactical asset allocation strategy that shifts allocations based on forecasts of outperformance of the satellite. Results are shown for different hit ratios of forecasts, based on a simulation of 1,000 scenarios.

and for maximum drawdown over all scenarios corresponds to the result for the average active manager according to our hypothetical hit ratios.

As exhibit 5 predictably shows, higher hit ratios lead to higher average expected returns. But the table also shows that the average for the maximum drawdown statistic computed across the 1,000 hypothetical managers is relatively high even in the presence of positive forecasting skill. For a hit ratio of 7/12, maximum drawdown is, on average, approximately -13%, a figure that reveals the impact of poor forecasts. In fact, even though these managers are right most of the time, they err five months a year, thus exposing the investor to significant downside risk.

The average value of risk and return statistics across 1,000 scenarios does not show the impact of manager-selection risk. Using a single manager leads to uncertainty, as results may be much better or much worse than the average across 1,000 managers. First, the results obtained by a single manager depend on the actual hit ratio for the sample period as opposed to his true long-term forecasting ability. Second, given a realised hit ratio, portfolio performance depends on the consequences of his

predictions. Predicting outperformance over a month during which the satellite underperforms by 1% is not the same as predicting outperformance over a month during which it underperforms by 10%, even though both are instances of forecast error. Likewise, predicting outperformance over a month during which the satellite outperforms by 10% is more valuable than predicting outperformance over a month during which it outperforms by 1%, though both are instances of forecast accuracy.

This dispersion of the managers with the same forecasting ability is shown in the lower panel of exhibit 6. The worst performing manager (or scenario) draws down a maximum of between -28% to

  • -

    16%, depending on the hit ratio we

assume. Likewise, the worst return over a one-year rolling period ranges from -23% to -13%, depending on the hit ratio. So it is clear that relying on active forecasting leads to additional risk, even if the manager is known to have positive forecasting skill.

The severe drawdowns shown even for managers with positive forecasting skill underscore the inability of these tactical allocation strategies to provide absolute return portfolios with smooth return profiles. Even with extremely high and

An EDHEC-Risk Institute Publication

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