What will we cover in this tutorial?
In this tutorial we will explore some aspects of the Pandas-Datareader, which is an invaluable way to get data from many sources, including the World Bank and Yahoo! Finance.
In this tutorial we will investigate if the GDP of a country is correlated to the stock market.
Step 1: Get GDP data from World Bank
In the previous tutorial we looked at the GDP per capita and compared it between countries. GDP per capita is a good way to compare country’s economy between each other.
In this tutorial we will look at the GDP and using the NY.GDP.MKTP.CD indicator of GDP in current US$.
We can extract the data by using using the download function from the Pandas-datareader library.
from pandas_datareader import wb gdp = wb.download(indicator='NY.GDP.MKTP.CD', country='US', start=1990, end=2019) print(gdp)
Resulting in the following output.
NY.GDP.MKTP.CD country year United States 2019 21427700000000 2018 20580223000000 2017 19485393853000 2016 18707188235000 2015 18219297584000 2014 17521746534000 2013 16784849190000 2012 16197007349000 2011 15542581104000
Step 2: Gathering the stock index
Then we need to gather the data from the stock market. As we look at the US stock market, the S&P 500 index is a good indicator of the market.
The ticker of S&P 500 is ^GSPC (yes, with the ^).
The Yahoo! Finance api is a great place to collect this type of data.
import pandas_datareader as pdr import datetime as dt start = dt.datetime(1990, 1, 1) end = dt.datetime(2019, 12, 31) sp500 = pdr.get_data_yahoo("^GSPC", start, end)['Adj Close'] print(sp500)
Resulting in the following output.
Date 1990-01-02 359.690002 1990-01-03 358.760010 1990-01-04 355.670013 1990-01-05 352.200012 1990-01-08 353.790009 ... 2019-12-24 3223.379883 2019-12-26 3239.909912 2019-12-27 3240.020020 2019-12-30 3221.290039 2019-12-31 3230.780029
Step 3: Visualizing the data on one plot
A good way to see if there is a correlation is simply by visualizing it.
This can be done with a few tweaks.
import pandas_datareader as pdr import pandas as pd import matplotlib.pyplot as plt import datetime as dt from pandas_datareader import wb gdp = wb.download(indicator='NY.GDP.MKTP.CD', country='US', start=1990, end=2019) gdp = gdp.unstack().T.reset_index(0) gdp.index = pd.to_datetime(gdp.index, format='%Y') start = dt.datetime(1990, 1, 1) end = dt.datetime(2019, 12, 31) sp500 = pdr.get_data_yahoo("^GSPC", start, end)['Adj Close'] data = sp500.to_frame().join(gdp, how='outer') data = data.interpolate(method='linear') ax = data['Adj Close'].plot() ax = data['United States'].plot(ax=ax, secondary_y=True) plt.show()
The GDP data needs to be formatted differently, by unstack’ing, transposing, and resetting the index. Then the index is converted from being strings of year to actually time series.
We use a outer join to get all the dates in the time series. Then we interpolate with a linear method to fill out the gab in the graph.
Finally, we make a plot af Adj Close of S&P 500 stock index and on of the GDP of United States, where we use the same graph, but using the secondary y-axis to plot. That means, the time series on the x-axis is the same.
The resulting graph is.
It could look like a correlation, which is visible in the aftermath of 2008.
Step 4: Calculate a correlation
Let’s try to make some correlation calculations.
First, let’s not just rely on how US GDP correlates to the US stock market. Let us try to relate it to other countries GDP and see how they relate to the strongest economy in the world.
import pandas_datareader as pdr import pandas as pd import matplotlib.pyplot as plt import datetime as dt from pandas_datareader import wb gdp = wb.download(indicator='NY.GDP.MKTP.CD', country=['NO', 'FR', 'US', 'GB', 'DK', 'DE', 'SE'], start=1990, end=2019) gdp = gdp.unstack().T.reset_index(0) gdp.index = pd.to_datetime(gdp.index, format='%Y') start = dt.datetime(1990, 1, 1) end = dt.datetime(2019, 12, 31) sp500 = pdr.get_data_yahoo("^GSPC", start, end)['Adj Close'] data = sp500.to_frame().join(gdp, how='outer') data = data.interpolate(method='linear') print(data.corr())
Where we compare it the the GDP for some more countries to verify our hypothesis.
Adj Close Denmark France Germany Norway Sweden United Kingdom United States Adj Close 1.000000 0.729701 0.674506 0.727289 0.653507 0.718829 0.759239 0.914303 Denmark 0.729701 1.000000 0.996500 0.986769 0.975780 0.978550 0.955674 0.926139 France 0.674506 0.996500 1.000000 0.982225 0.979767 0.974825 0.945877 0.893780 Germany 0.727289 0.986769 0.982225 1.000000 0.953131 0.972542 0.913443 0.916239 Norway 0.653507 0.975780 0.979767 0.953131 1.000000 0.978784 0.933795 0.878704 Sweden 0.718829 0.978550 0.974825 0.972542 0.978784 1.000000 0.930621 0.916530 United Kingdom 0.759239 0.955674 0.945877 0.913443 0.933795 0.930621 1.000000 0.915859 United States 0.914303 0.926139 0.893780 0.916239 0.878704 0.916530 0.915859 1.000000
Now that is interesting. The US Stock market (Adj Close) correlates the strongest with the US GDP. Not surprising.
Of the chosen countries, the Danish GDP is the second most correlated to US stock market. The GDP of the countries correlate all strongly with the US GDP. There Norway correlates the least.
Continue the exploration of World Bank data.
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