historical P/B and market value data in datastream PyDSWS

Hi,
I am trying to download historical P/B and market value data for STOXX Europe 600 constituents via PyDSWS.
data = ds.get_data("LDJSTOXX0301",['ISIN',"NAME",'P',"WTIDX","PTBV",'MV',"SDN#(LN#(X/LAG#(X,1M)),36M)"],''2001-03-30")
this query returns 48 NAs for PTBV
I have 3 questions
1) is it save to assume that only accounting data available at that point in time is used and that data is not backfilled? E.g. Book value on 3/30/2001 is based on latest accounting data that was available at that point in time?
2) how can I adjust market value for fx (i.e. base it in EUR)?
3) why are there so many NA for PTBV? (there are similarly as much NAs for more recent points in time)
Many thanks,
Steffen
Best Answer
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Hi @steffen.fuchs So I opened a case number: 12216420 on your behalf and they will respond very shortly whilst they are investigating.
Regarding your question 1) I think it is safe to assume that they would use point in time data for historical ratios. 2) So I am not sure if there is an easy way to do this in the service - I did note that there are two currency fields available 'ACUR' & 'ECUR' which give the currency of Accounts and Earnings respectively - so its possible to do a conversion on the data if you have the origin currency and target currency - but this would be a separate step - I will check with my colleague to confirm this and get back.
I hope this can help.
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Answers
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Hi all - I have escalated this to the DataStream team via email as this is a content query and they will respond directly to this post. I was able to replicate the issue and I sent them a spreadsheet extract of the dataframe containing 4200 rows - so all the NAs were easily visible.
df = ds.get_data(tickers="LDJSTOXX0301|L",fields=["ISIN","NAME","P","WTIDX","PTBV","MV","SDN#(LN#(X/LAG#(X,1M)),36M)"],kind=0,start="2001-03-30")
dfPlease bear with us.
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How about my questions 1 and 2?
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