Are Stock Prices a Random Walk?

Most stock prices follow a random walk (perhaps with a drift). You will look at a time series of Amazon stock prices, pre-loaded in the DataFrame AMZN, and run the 'Augmented Dickey-Fuller Test' from the statsmodels library to show that it does indeed follow a random walk.

With the ADF test, the "null hypothesis" (the hypothesis that we either reject or fail to reject) is that the series follows a random walk. Therefore, a low p-value (say less than 5%) means we can reject the null hypothesis that the series is a random walk.

This exercise is part of the course

Time Series Analysis in Python

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Exercise instructions

Import the adfuller module from statsmodels.
Run the Augmented Dickey-Fuller test on the series of closing stock prices, which is the column 'Adj Close' in the AMZN DataFrame.
Print out the entire output, which includes the test statistic, the p-values, and the critical values for tests with 1%, 10%, and 5% levels.
Print out just the p-value of the test (results[0] is the test statistic, and results[1] is the p-value).

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import the adfuller module from statsmodels
from statsmodels.tsa.stattools import adfuller

# Run the ADF test on the price series and print out the results
results = adfuller(___)
print(results)

# Just print out the p-value
print('The p-value of the test on prices is: ' + str(results[___]))

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