Are Stock Returns Predictable? A Test Using Markov Chains




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    • McQueen is with the Marriott School of Management, Institute of Business Management, Brigham Young University. Thorley is with the Department of Finance, Graduate School of Business Administration, University of Washington. We are grateful to Haim Levy, J. Michael Pinegar, V. Vance Roley, Andrew Siegel, Simon Wheatley, an anonymous referee, the editor (René Stulz), and seminar participants at the University of Washington and Brigham Young University.


This paper uses a Markov chain model to test the random walk hypothesis of stock prices. Given a time series of returns, a Markov chain is defined by letting one state represent high returns and the other represent low returns. The random walk hypothesis restricts the transition probabilities of the Markov chain to be equal irrespective of the prior years. Annual real returns are shown to exhibit significant nonrandom walk behavior in the sense that low (high) returns tend to follow runs of high (low) returns in the postwar period.