Flirting With Models

Now if we check our prediction error, where returns are sorted relative to their variance over the period the returns were generated, we see more stability.

However, at the end of the day, we are only working with ~30 assets. So let’s look at the 6-1-1 region (which seems relatively stable). We are effectively looking at an error of ~(265/30)=8.3 per asset. i.e. The mean prediction error in index for each asset, at each step, is about 8.3 spots. That … isn’t very good. In fact, that is about 25% of the assets…

Perhaps if instead of predicting the exact index, we looked at deciles?