Is there a possibility to do a regression/correlation analysis of ESG KPIs and the ESG Score usin...

...g API to see if these values influence each other?

Client wants to pull ESG KPIs and the ESG Score through API and do a regression analysis to see if the values affect each other.

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Accepted answers

jonathan.legrand

Hi @grae.tejada, Please note that this Forum is for technical LSEG API questions only. For answers about content, such as the data item names (e.g.: what is the name of a "ESG KPI" field in the LDL), please use the DIB; failing that, please reach out to my.refinitiv.com.

I looks into the DIB myself and tested out an answer for you with the fields ["TR.TRESGScore","TR.TRESGCControversiesScore"], and came up with:

df = ld.get_history(
    universe="LSEG.L",
    fields=["TR.TRESGScore","TR.TRESGCControversiesScore"],
    start="2020-01-01",
    end="2024-09-01")

df.corr()

This is the output; is it what you're after?

You can go further in your analysis with:

# Assuming df contains the necessary columns
X = df[['TR.TRESGCControversiesScore']]
y = df['TR.TRESGScore']


# Handle any missing values if necessary
X = X.fillna(X.mean())
y = y.fillna(y.mean())


# Initialize and fit the regression model
model = LinearRegression()
model.fit(X, y)


# Print the coefficients
print("Coefficient:", model.coef_[0])
print("Intercept:", model.intercept_)


# Predict ESG Score based on Controversies Score
y_pred = model.predict(X)


from sklearn.metrics import r2_score


r2 = r2_score(y, y_pred)
print("R-squared:", r2)

Don't hesitate to let me know what Data Items you would prefer using, and I can entre them in the code above for you. Alternatively, I would suggest you do so yourself using Codebook.

All comments

jonathan.legrand

I looks into the DIB myself and tested out an answer for you with the fields ["TR.TRESGScore","TR.TRESGCControversiesScore"], and came up with:

df = ld.get_history(
    universe="LSEG.L",
    fields=["TR.TRESGScore","TR.TRESGCControversiesScore"],
    start="2020-01-01",
    end="2024-09-01")

df.corr()

This is the output; is it what you're after?

You can go further in your analysis with:

# Assuming df contains the necessary columns
X = df[['TR.TRESGCControversiesScore']]
y = df['TR.TRESGScore']


# Handle any missing values if necessary
X = X.fillna(X.mean())
y = y.fillna(y.mean())


# Initialize and fit the regression model
model = LinearRegression()
model.fit(X, y)


# Print the coefficients
print("Coefficient:", model.coef_[0])
print("Intercept:", model.intercept_)


# Predict ESG Score based on Controversies Score
y_pred = model.predict(X)


from sklearn.metrics import r2_score


r2 = r2_score(y, y_pred)
print("R-squared:", r2)

Don't hesitate to let me know what Data Items you would prefer using, and I can entre them in the code above for you. Alternatively, I would suggest you do so yourself using Codebook.

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