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Alina Steshkova avatar image
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I/B/E/S EPS and LTG forecasts

Dear Community,

I have been trying to understand the data content and coverage of I/B/E/S forecasts for General Motors. Using the following code, I am only able to retrieve data starting from 2010, presumably due to the company's restructuring that occurred around that time. However, I’m surprised that LSEG does not provide historical data prior to this event, particularly for such a large and established company like GM.

ek.get_data('GM.N', ['TR.LTGMean.date', 'TR.LTGMean', 'TR.LTGMedian'], {"SDate":" 1968-01-31 ", "EDate" : "0", Frq" : "M"})

I find it hard to believe that there is no coverage of Long-Term Growth (LTG) and EPS forecasts before 2010. Has anyone else encountered this issue? Is there any way to access the historical forecasts prior to major company events, or does anyone know of a manual that provides more detailed information on I/B/E/S coverage, given its extensive data?

Thank you in advance for any insights!

Best regards,
Alina

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Jirapongse avatar image
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@Alina Steshkova

Thank you for reaching out to us.

I found this one:

df, err = ek.get_data('GM.N^F09',
                      ['TR.LTGMean.date', 'TR.LTGMean', 'TR.LTGMedian'], 
                      {"SDate":"1968-01-31 ", "EDate" : "0", "Frq" : "M"})
df.dropna().drop_duplicates()

However, you need to contact the helpdesk team directly via MyAccount to confirm it.

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Alina Steshkova avatar image
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@Jirapongse

Thank you very much for your answer! It works perfectly! I was wondering if it is possible to search the entire I/B/E/S database, as I have hundreds of companies facing similar issues to GM, but there is no intuitive way to identify the RICs.

Is there also any available documentation for I/B/E/S database? Unfortunately, the API link you provided in another topic for I/B/E/S is no longer active.

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Jirapongse avatar image
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@Alina Steshkova

You can try the Search API in the LSEG Data Library for Python. The examples are on GitHub.

The code looks like this:

df = ld.discovery.search(
        view = ld.discovery.Views.EQUITY_QUOTES,
        filter = "SymbolHistoryScope(SymbolType eq 'RIC' and SymbolValue eq 'GM.N')",
        select = "RIC,SymbolHistoryScope(filter:(SymbolType xeq 'RIC'))",
        top = 10
    )
df

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