||Recent studies on stock market pricing have rejected the random walk model for short-term periods and have concentrated on long-term persistent or mean-reverting dependence. The problem with these studies is that their statistical results can be biased by the shorter term dependence. Rather than trying to develop a unified theory that explains both short- and long-term dependence, current studies use different methodologies to correct for the short-term dependence while trying to test for long-term dependence. This paper uses a sequential information theory to focus attention on short-term dependence effects. This theory states that the market process is a nonstationary mean process surrounded by a nonstationary autocovariance error process. A nonstationary mean process implies short-term dependence resulting from changing economic events (new information). Long-term persistent dependence then derives from nonperiodic economic cycles. A new empirical approach, a cross-sectional autocorrelation coefficient is used since it is free from the stationarity problems of previous techniques.