Since the 2008-09 financial crisis, the value of using quant factors for stock selection has been questioned. However, recent studies by the StarMine quant research team show that quant factors are again working very well, exhibiting both long-term outperformance and remarkable gains during the past 12 months.:
Chief among the many difficulties a portfolio manager faces is how to reconcile conflicting views on expected returns when selecting securities. The problem may flare up when members of the investment committee have differing views on a particular stock, but choices also arise for the lone manager who runs a strictly quantitative strategy based on multiple models. Like the investment committee members, each quant model will have a different ranking of expected returns that needs to be resolved. This research note illustrates a simple strategy for combining quant models and offers some alternative techniques for enhancing portfolio returns.
For the purposes of this note, we assume a single portfolio manager uses multiple quant models to do equity portfolio selection, and each quant model outputs a ranked score. StarMine equity and credit risk models output 1-100 scores, with 100 being a top-ranked company. Hence, they provide enough resolution to distinguish many shades of predicted performance while placing all models scores on the same normalized scale—a convenience when combining models.
I believe I shall take Alpha Now! as my new imperative and give up on:
Figure 1: Using top quintile Val-Mo stocks, rebalanced monthly, and optimizing filter thresholds for Earnings Quality and Short Interest performs very well on a universe of US small cap stocks with sufficient daily liquidity.
Suppose a portfolio manager wants to combine several such models to make a single stock ranking model....MORE
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