A useful evaluation metric is the rank information coefficient, often referred to as rank IC. The rank IC tells us whether the ranks of our alpha values are correlated with the ranks of the future returns. In other words, if the alpha factor suggested that we bet more on stock ABC and less on stock XYZ, was the future return of ABC relatively high? Was a future return of stock XYZ relatively low? If the future performance of the assets matched the expectations that was suggested by the alpha factor, then the information coefficient would be higher. Otherwise, it would be lower and possibly negative. For some insight, let’s first look at this without ranking. Let’s pretend there are just two stocks in our universe, ABC and XYZ. Stock ABC has a high positive alpha value and XYZ has a very negative alpha value as calculated before time, t. Between time T to time T plus one, the return of stock ABC is positive and XYZ’s return is negative. These alpha values appear to be correlated with the forward asset returns. Note here, when we say asset return, we’re referring to the return of each stock for each time period. We also specify that the asset return is a forward return. We say, forward asset return to specify that the return is in the future or forward in time compared to when the alpha value was calculated. So, if the alpha values are calculated before time t, then the forward asset return is calculated with data that occurs later from time t to time t plus one. To make our evaluation more robust, we want to use ranks instead of the original alpha values and returns. Stock ABC is alpha value before time t is high and positive, while stock XYZ is alpha value, is negative. So, ABC has a higher rank of two and XYZ has a lower rank of one. The forward asset return of ABC from time t to time t plus one is higher compared to XYZ. So, the rank of ABC’s forward asset return is two, and the rank of XYZ’s forward asset return is one. If the ranks of the alpha values and the ranks of the forward asset returns are highly correlated, the rank IC metric would be close to one. In this example, since the ranks of the two alpha values are equal to the ranks of the two forward asset returns, then the rank IC in this example is one.