Previous data, like
financial_leverage, can be fetched with the help of a small custom factor. Something like the following (there is also a relevant post with a similar issue here )
def compute(self, today, assets, out, input):
out[:] = input
# create a factor for financial_leverage 252 trading days ago (approx 1 calendar year)
leverage_prev = Previous(inputs=[Fundamentals.financial_leverage], window_length=252)
The Morningstar data is primarily based on the latest quarterly filing. So values like
roa will be the last quarter net Income / average total assets. The FactSet
FS_Fundamentals.roa_af will be the annual roa or last annual report net Income / total assets. It's probably best not to mix the two. A 'good' way to get a delta quarterly roa would be
roa = Fundamentals.roa.latest
roa_prev = Previous(inputs=[Fundamentals.roa], window_length=252)
delta_roa = roa / roa_prev
One could do the same thing with the FactSet annual data but it would only change annually. The quarterly Morningstar data will capture quarterly changes which may be good or bad depending on your application.
Now, I said this is a 'good' way, however simply looking back 252 trading days can lead to comparing the wrong quarters at times, especially near the dates where a quarterly report is released. To be more accurate, one would need to look at the
asof_date, subtract a year, then lookup the latest value with that previous
asof_date. A little more work but also a little more accurate.
Hope that helps.
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