Capturing Alpha In Risk Rewards - Morgan Stanley

Morgan Stanley has one of the most robust sets of scenario-based price target forecasts in the world with around 70,000 forecasts over 10 years. Naturally, they decided to evaluate the quality of their analysts’ forecasts and the results were positive. In the chart below, their price target, scenario-based strategies consistently created positive alpha.  
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The model was built by evaluating analysts’ scenario analysis to determine buy and sell signals by using measurements and trends on the variables of Downside, Tilt, and Uncertainty. The magnitude and number of those signals determined the weighting in the hypothetical portfolio.
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They determined that there was a demonstrable benefit in using a scenario-analysis instead of a single price target.

Breaking the analysis down to its components (individual scenario analyses) showed consistent predictive quality from the scenarios analysis as measured by the pre-cost hit ratio (the percentage of long/short signals that generate higher/lower returns than the total return of the equity index). While a mid-50s hit rate may seem marginal, it is substantial. It is enough to create consistent outperformance, as we measured by observing our managers with consistent hit rates above 50%.
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The Morgan Stanley analysis is substantive in two ways. First, it supports our research that scenario analyses have predictive power that can be utilized to create positive alpha strategies. The second is our suspicion that buy-side manager scenario analyses are superior to sell-side forecasts because of their real-world application, their lack of administrative constraints, and lack of investment-banking conflict. If that is the case, Alpha Theory forecast dataset should have predictive power superior to that in the Morgan Stanley analysis.

This article is one of a long series of “Empirical Proofs” of active manager skill that we’ve been collecting. To see the full list, download a full version of the Concentration Manifesto.