You may notice, that if the amount we invest in each stock of our portfolio is tied to the Alpha value that we get from daily data, then we would be constantly buying and selling every day in order to follow the signal faithfully. In other words, as the Alpha vector changes every day, we’d have to adjust our portfolio weights every day also. We also have to address what happens to our Alpha vector when we encounter outliers, or extreme values in the data. If we have had a large increase in the office signal for one stock, then a sharp decrease the next day, this would effectively tell us to buy a lot of that stock and then sell a lot of that stock the next day. This may or may not be warranted. In the real world, trading costs money. So, we want to be very confident that if we go to make a trade and bear that cost, that it is indeed warranted. Some ways to keep extreme values from leading to unnecessarily large trades, are by clipping very large and small values at for example the 95th percentile and the fifth percentile. This process is called winsorizing. Here’s an example of winsorizing in Alpha vector which has Alpha values for each stock, for a single day. For any number that exceeds the 95th percentile, we’ve replaced that outlier with a number at the 95th percentile. Also for any values that are below the fifth percentile, we replace those with a number at the fifth percentile. Again, this is called winsorizing. We can also deal with outliers, by setting a maximum magnitude allowed waits for any single stock. Note that we would handle outliers for each Alpha vector which may be updated each day. Even when we’ve dealt with outliers, there’s still the issue of whether it makes sense to buy and sell based on the signal if their relative magnitudes for the Alpha values don’t change. Let’s again take the example of Apple and Alphabet. One day, Apple’s Alpha value is 0.33 and Alphabet’s value is 0.31. What if on the next day, Apple’s Alpha value increased by 0.01 and Alphabet’s Alpha value also increased by 0.01. If we translated these directly into portfolio weights, the weights would change slightly from day one today two. But the important thing to notice is that we’d still be putting more money on Apple relative to Alphabet. So, maybe we wouldn’t actually want to change our positions at all. Often what we want to do, is have a more robust version of the signal which is able to withstand outliers, handle noise in the data, and also keep us from making potentially excessive traits. We can handle this with a ranking.