9. Subjective Scoring Model

 

Choice of Factors:

 

We reviewed the predictive strength of all the factors for the in sample period to determine a subjective scoring system used to weight each factor.  These weights were then used to produce a trading strategy for the out of sample years.  The factors we used are:

 

The best performing factors were LTM EPS Yield and Forward year earnings yield.  Both of these factors had good step functions from the top quartile to the bottom, and performed well in a long-short trading strategy.  These factors are both indicators of growth, and they are strongly correlated.  It makes sense that if a stock has had solid earnings yields over the past twelve months that analysts would estimate strong yields for the next year as well.  Diagnostically FY1 Yield was slightly better so that is the factor we chose. 

 

The next factors to show promise were those based on analysts revisions.  We chose the FY1 up versus down revision ratio, primarily because the other factor, 3 month revisions, had a very high turnover which would lead to high transaction costs.  This factor would catch those stocks with improving expectations, that don’t necessarily pay dividends and don’t (yet) have good earnings yields. 

 

We chose Dividend Yield as our third factor because it performs decently in predicting the top portfolio and it is very uncorrelated with the other two factors.  Dividend yield could capture more of the value stocks that wouldn’t be reflected in the other two.

 

Scoring:

 

Each portfolio within each factor was scored on a scale of one to five based on our assessment of its ability to predict returns, both good and bad, in sample.  On the extremes, those with the best positive predictions received a score of five, and those with the best negative predictions would receive a score of -5.  If a factor showed no predictability for returns in a certain portfolio it would not be scored.  We distributed the following scores:

 

Analysis

 

Equal Weighted:           In sample, implementing a trading strategy based on the subjective scores did very well, with an annualized average return of 33.16% for the top portfolio and 9.35% for the bottom portfolio.  The long-short return would be 23.8%, and very well hedged with betas of 1.03 and .99 for the top and bottom portfolios respectively.  One concern would be the percentage turnover for the portfolios, particularly the bottom one, at 20.96% and 39.00% respectively.  Such turnovers would result in high transaction costs that could eat into the returns.

 

                                    Out of sample this model performed well 4 out of the 5 years, with 1999 being the negative year.  By implementing this strategy, we would have had a loss of 25% in 1999, but average gains of more than 50% in each of the other years.  

 

Value Weighted:           In sample, the value weighted portfolios performed similarly to the equal weighted ones.  The returns are slightly lower, at 28.29% for portfolio 1 and 10.36% for portfolio 5.  However, the standard deviations are also lower than for equal weighted.  The betas are not as evenly matched, but still give a good hedge with a beta of 1.09 at the top and .93 at the bottom. 

 

                                    Out of sample, this strategy performs extremely well.  Similar to the equal weighted, in 1999 a long-short strategy based on this model would lose money, but only 8%.  For the remaining years we would achieve an average of 36% returns annually, with no disastrous years. 

           

Conclusion:

 

Using a combination of factors we developed a good model for a long-short trading strategy.  The factors capture elements of growth and value stocks and seem to do well in various market conditions, with decent upside and limited downside.  We would probably implement this model using a value weighted portfolio because it seems to hedge the downside better.  The one major concern with this model is the potential transactions costs due to turnover.