Based upon the results of our global macro scan; we apply a set of analytical factors to further refine our results into silos of long/short investment opportunities that should offer the strongest performance.

Among the key considerations are whether an ETF is overbought/sold, and its relative performance versus its peer group.

By actively taking positions with low, or even negative correlation to each other, we reduce risk. As opposed to taking the middle road and accepting losses as part of typical portfolio behaviour; we polarize the portfolio to capitalize on market movement regardless of direction.

When determining whether an ETF is statistically overbought or oversold, our quantitative modeling performs an evaluation of the historical performance of the ETF. In this example, when the selling intensity of the ETF’s components exceeded 3 standard deviations of significance (SD-3), it suggests a high probability buying opportunity is at hand. Case in point, in each instance over the past 6 years where the XGD components exceeded 3 standard deviations of significance, it subsequently posted gains in the 1-3 months immediately following.

A similar methodology is applied toward identifying overbought conditions and probable turning points.