Imperfect Information and Cross-Autocorrelation among Stock Prices



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    • Arizona State University. I thank Hank Bessembinder, James Booth, K. C. Chan, Michael Hertzel, Tim Mech, Richard Smith, Jiang Wang, an anonymous referee, Journal editors David Mayers and René Stulz, and seminar participants at Arizona State University and the 1991 Western Finance Association Meeting for valuable comments, and Beth Baugh for editorial assistance. Any errors are, of course, my responsibility.


I develop a model to explain why stock returns are positively cross-autocorrelated. When market makers observe noisy signals about the value of their stocks but cannot instantaneously condition prices on the signals of other stocks, which contain marketwide information, the pricing error of one stock is correlated with the other signals. As market makers adjust prices after observing true values or previous price changes of other stocks, stock returns become positively cross-autocorrelated. If the signal quality differs among stocks, the cross-autocorrelation pattern is asymmetric. I show that both own- and cross-autocorrelations are higher when market movements are larger.