Predicted Factor/Smart Beta Alphas

Which equity factors have high and low expected returns? In their February 2017 paper entitled "Forecasting Factor and Smart Beta Returns (Hint: History Is Worse than Useless)", Robert Arnott, Noah Beck and Vitali Kalesnik evaluate attractiveness of eight widely used stock factors. They measure alpha for each factor conventionally via a portfolio that is long (short) stocks with factor values having high (low) expected returns, reformed systematically. They compare factor alpha forecasting abilities of six models:

1. Factor return for the last five years.
2. Past return over the very long term (multiple decades), a conventionally used assumption.
3. Simple relative valuation (average valuation of long-side stocks divided by average valuation of short-side stocks), comparing current level to its past average.
4. Relative valuation with shrunk parameters to moderate forecasts by dampening overfitting to past data.
5. Relative valuation with shrunk parameters and variance reduction, further moderating Model 4 by halving its outputs.
6. Relative valuation with look-ahead full-sample calibration to assess limits of predictability. 

They employ simple benchmark forecasts of zero factor alphas. Using 24 years of specified stock data (January 1967 - December 1990) for model calibrations, about 20 years of data (January 1991 - October 2011) to generate forecasts and the balance of data (through December 2016) to complete forecast accuracy measurements, they find that: