An Analysis of the Random Walk Hypothesis based on Stock Prices, Dividends, and Earnings

Risa Leigh Kavalerchik


This paper explores the stationarity of price movements, dividend yields, and earnings yields for stock market indices and individual stocks within the broader context of the random walk hypothesis. In general, in order for a stock’s price to follow a random walk, its future price must be unforecastable based on all currently available information in the stock market, including its price history. If a stock price is stationary in a given time period, its statistical process does not change over time, meaning that the series has a deterministic trend, which could even be flat. This investigation tests for stationarity in the time series of prices and dividend yields of the Dow Jones Industrial Average (DJIA), the S&P 500 Index, and their underlying component stocks based on the results of univariate and panel unit root tests. I also test for the stationarity of earnings yields for the components of the DJIA. I find that prices of the DJIA and its underlying components behave in a more stationary manner than do the prices of the S&P 500 and its underlying components. Dividend yields behave in an equally non-stationary fashion for the underlying components of both the DJIA and S&P 500. Interestingly, earnings yields for the DJIA prove to exhibit more stationarity than the dividend yields for the DJIA and S&P 500, suggesting that earnings data have some predictability for stock prices.


Stock Market Stationarity

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