As practitioners, we were reading academic research papers for insights. So, it’s helpful to know how academic papers and practitioners view quantiles differently. Academic papers tend to take raw alpha signals, which are not ranked, and split them into quantiles only. Their analysis usually focuses on the tails. That is, the highest and lowest quantiles. The reason that academic researchers are focused on this, is that they want to detect broadly applicable market phenomenon, and are not necessarily interested in how to implement a trading strategy. Because of this, you may notice that certain academic research will find an effect in just one tail such as the lowest quintile, while they find less of an effect in the other tail quantile or even in the other quantiles in between. Note that this means that the abnormal returns that they detect in such a paper, may only work on a subset of the stock universe. Practitioners have the goal of implementing a trading strategy that can be applied to all the assets in their universe. This means that with quintile analysis, practitioners care about how the factor performs across all quantile groups, not just one or two tails. This is because practitioners want to see how the factor influences all stocks and there trading universe. So, even if a research study found that an alpha factor only worked in the subset of the quantile distribution, the practitioner will view this as a graded spectrum in order to generate an alpha number for every stock in their universe.