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Picking factors for an algorithm using the fundamental factor models lecture.

Hi,
Sorry for the very basic question as I am new to this. Nevertheless...

I am using Quantopian to make my first algorithm and have been stuck on doing some factor analysis for the past few days. In the lecture on fundamental factor models, they show how to estimate the risk premiums for your factors, using some code for regressions on returns.
I understand that the estimates for risk premia is the additional reward in your algorithm you get for exposing yourself to that fundamental factor. However, when analysing a basic 6-month momentum factor I get a negative risk premium coefficient using the code in the lecture. Despite this, when analysing the factor individually using alphalens over the same time frame, it clearly has some positive predictive power of returns, (the 1st and 5th quantiles of equities have clear positive (0.59) and negative (-0.16) mean averages of returns).

Why is this?
and...
Should you t-test the risk premiums to decide if factors are statistically significant, or the coefficients from the initial regression of your factors against returns?

Thanks for the help.

1 response

Hi Michael,

Welcome! I'd recommend posting this question in the recent Alphalens Questions thread. I have a lot of similar questions, and I've posted all of mine there.