I am a new aspiring quant who is trying to build a fundamental factor algorithm to rank stocks for a basic long/short strategy, so sorry for the likely very basic question. Nevertheless...

Why do you need to regress fundamental factors you would like to test against portfolio returns constructed of equities of the highest and lowest extremes of those values in your trading universe. Why not just regress the factors against the returns of your whole trading universe?

Is this just because it is too computationally expensive or does the construction of those groups of equities serve another function?



If I understand you correctly, you don’t need to build the groupings, but the construction of the groups of equities allows you to account for any potentially non-linear effects in the response of equities to your factor. It can also help to make your model more robust.

For example, people often talk about a size factor, but using raw market capitalisation would give you a factor that is dominated by one or two names (Apple,Google, etc. in the US market) if you use OLS regression. It is not clear that there is a linear response to a market cap factor so people often use log(market cap), but that choice may not be so obvious. That leaves you with a choice of how to transform your factor. Given that, it may be better to go via these baskets of extreme securities to model the factor.


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