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yaokoffi.kouassi avatar image
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Retrieve/Calculate the BETA of a stock relative to an index other than its benchmark

Hi guys,

A customer is looking for an easiest way to compute the BETA of a stock relative to any index this stock may be in.


For instance, let's take Volkswagen whose RIC is VOWG_p.DE. Volkswagen's main index is the DAX whose RIC is .GDAXI

However, Volkswagen is also part of the Euro STOXX 600 whose RIC is .STOXX

As the Euro Stoxx 600 is broadly used as a benchmark, it makes sense to be able to retrieve/compute performance data linked to it.

In that case, what does the underlying python function which uses refinitiv.data library look like ?

Thanks




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Jirapongse avatar image   Jirapongse

@yaokoffi.kouassi

Hi,

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Thanks,

AHS

raksina.samasiri avatar image   raksina.samasiri

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Thanks,
AHS

2 Answers

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yaokoffi.kouassi avatar image
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Following @haykaz.aramyan contribution on this thread, this allows me to compute the BETA.

Thanks

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nick.zincone avatar image
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Hi @yaokoffi.kouassi

You may need to elaborate exactly what specific details you need. It's unclear if you want some specific python function to calculate a value for a specific index or whether you need a way to determine the list of indexes a specific RIC is a member of. For the latter, I believe the following is a way to retrieve this list:

As you stated, the main index for VOWG_p.DE is .GDAXI.

rd.discovery.search(  
    filter = "code eq 'VOWG_p.DE' and IsMemberOfIndex eq true",
    select = "DocumentTitle, MainIndexRIC, IndexMembership"
)

1691768629733.png

And based on the Index Membership, we can pull down the list of index details:

rd.discovery.search(
    top = 500,
    filter = "NDAID in ('276262' '293074' '296158' '303064' '307126' '330500' '335612' '354837' '355640' '355787' '363702' '363735' '364172' '365089' '366554' '382578' '384349' '384370' '414779' '414990' '415264' '415267' '415442' '415453' '415534' '415536' '415539' '415543' '416038' '431316' '431417' '453413' '548592' '5847432' '7182968' '7183011' '7183056' '7183102' '7191327' '7192074' '7192089' '7192092' '7192095' '7192098' '7456134' '11459440' '12398405' '12398409' '15464123' '18973288' '19070454' '19070472' '19070490' '19070508' '60789514' '60789515' '60789519' '60789521' '60789523' '62093664' '63124495' '63124515' '63124557' '63124573' '63124613' '63269979' '65833503' '65833507' '65836585' '65836588' '65836738' '65836742' '65836904' '65836910' '65837092' '65837096' '65837589' '65837592' '74629512' '74630188' '74632043' '74632045' '91634410' '91634564' '122266313' '124541486' '126738417' '136588784' '136591023' '152728676' '152728678' '158704891' '158706172' '168347385' '181282521' '181282526' '192154904' '192156113' '192161327' '192164530' '205293444' '238657872' '238657873' '238657874' '289858120' '297987784' '357637055' '357637131' '357637163' '357637209' '357637253' '357637269' '357637345' '357637707' '460786934')",    
    select = "DocumentTitle, RIC"
)

1691768754769.png


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