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Hi guys,
This is a general question following this thread.
Given a stock (let's say VOWG_p.DE) and a reference index (let's say .STOXX), a customer wants to compute/retrieve the correlation between the day to day return of the stock and the reference index price on a timeframe. Of course the stock returns include the dividends
what does a a python function which uses refinitiv.data library look like?
Hi @yaokoffi.kouassi ,
I am afraid there is no direct way of computing the correlation coefficient from the library. If I got your question correct, these are the steps I would take to calculate the coefficient:
1. Get the prices of the stock and the benchmark from Refinitiv.data:
index_price = rd.get_history(universe = '.STOXX', fields='TRDPRC_1', start = '2023-07-01', end = '2023-08-14') stock_price = rd.get_history(universe = 'VOWG_p.DE', fields='OFF_CLOSE', start = '2023-07-01', end = '2023-08-14')
2. Calculate daily returns:
index_return = index_price['TRDPRC_1'].pct_change().dropna() stock_return = stock_price['OFF_CLOSE'].pct_change().dropna()
3. Calculate the required correlation coefficient (e.g. Pearson, spearman) using Scipy library:
import scipy.stats scipy.stats.pearsonr(index_return.values, stock_return.values)
This returns the the coefficient and the p-value:
(0.45796402563762706, 0.012482344923260497)
Hope this helps and please let me know if I missed anything while building my answer.
Best regards,
Haykaz
You can also take the approach with the ADC computation capabilities to calculate beta:
rd.get_data("VOWG_p.DE","RSLOPE('.STOXX',PERCENT_CHG(TR.PriceClose(Sdate=-5Y,Edate=0D,Curn=EUR,Frq=M),Lag=-1M))")
