I'm a quant working in a mainly fundamental shop. Analysts are asked to score things like management or industry trends of stocks in their "watchlist", and I am now trying to weave the results into a factor model. The model includes other factors such as earnings yield and some financial quality metrics.
The scores are simple 1 to 5 scores, where 1 is very good and 5 is very bad. A lot of thought is put into them, and they derive from team consensus (and hours of arguments).
The problem is that they are heavily skewed towards the better end of the spectrum. Stocks which would score a 5 would be considered so bad we wouldn't bother watching them (and thus are not even in our watchlist). Stocks which score a 4 are rare: only a couple of stocks, one of which has had substantial volatility.
Which statistical technique best deals with the skew in score distributions?
When I compute factor premiums with OLS, I get a very low R-squared.