Eficient Capital Markets 393 TABLE 1 (from [10]) First-order Serial Correlation Coefficients for One, Four-, Nine-, and Sixteen-Day Changes in Loge Price Differencing Interval (Days) Stock Four Allied Chemical American Can 087* a. t. t 039 American tobacco Anaconda 202 Bethlehem Steel 112 Du Pont Eastman Kodak 1732351 c International Harvester International Nickel Johns Manville Owens illinois 3410000 Sears 261 Standard Oil(NJ) 121 &:C 118 094* ,178 United Aircraft Woolworth .033 s Coefficient is twice its computed standard error. For example, Table 1(taken from [10])shows the serial correlations be- tween successive changes in the natural log of price for each of the thirty tocks of the Dow Jones Industrial Average, for time periods that vary slightl from stock to stock, but usually run from about the end of 1957 to September 26, 1962. The serial correlations of successive changes in loge price are shown for differencing intervals of one, four, nine, and sixteen days ommon differencing intervals of a day, a week or a month, are trivial relative to other sources of ariation in returns. Later, when we consider Rolls work [37], we shall see that this is not true for one week returns on U.s. Government Treasury Bills 13. The use of changes in log. price as the measure of return is common in the random walk literature. It can be justified in several ways. But for current purposes, it is sufficient to note that for price changes less than fifteen per cent, the change in log price is approximately the percentage price change or one-period return. And for differencing intervals shorter than one month, returns in excess of fifteen [10] reports that for the data of 1, te carried out on percentage or one-period returns yielded results essentially identical to the tests based on changes in loge pric