when calculating autocorrelation the result can range from 1 to 1 
an autocorrelation of 1 represents a perfect positive correlation an increase seen in one time series leads to a proportionate increase in the other time series on the other hand an autocorrelation of 1 represents a perfect negative correlation an increase seen in one time series results in a proportionate decrease in the other time series autocorrelation measures linear relationships even if the autocorrelation is minuscule there can still be a nonlinear relationship between a time series and a lagged version of itself the most common method of test autocorrelation is the durbin watson test without getting too technical the durbin watson is a statistic that detects autocorrelation from a regression analysis the durbin watson always produces a test number range from 0 to 4 values closer to 0 indicate a greater degree of positive correlation values closer to 4 indicate a greater degree of negative autocorrelation while values closer to the middle suggest less autocorrelation correlation measures the relationship between two variables whereas autocorrelation measures the relationship of a variable with lagged values of itself so why is autocorrelation important in financial markets simple autocorrelation can be applied to thoroughly analyze historical price movements which investors can then use to predict future price movements specifically autocorrelation can be used to determine if a momentum trading strategy makes sense autocorrelation in technical analysisautocorrelation can be useful for technical analysis that s because technical analysis is most concerned with the trends of and relationships between security prices using charting techniques this is in contrast with fundamental analysis which focuses instead on a company s financial health or management technical analysts can use autocorrelation to figure out how much of an impact past prices for a security have on its future price autocorrelation can help determine if there is a momentum factor at play with a given stock if a stock with a high positive autocorrelation posts two straight days of big gains for example it might be reasonable to expect the stock to rise over the next two days as well example of autocorrelationlet s assume rain is looking to determine if a stock s returns in their portfolio exhibit autocorrelation that is the stock s returns relate to its returns in previous trading sessions if the returns exhibit autocorrelation rain could characterize it as a momentum stock because past returns seem to influence future returns rain runs a regression with the prior trading session s return as the independent variable and the current return as the dependent variable they find that returns one day prior have a positive autocorrelation of 0 8 since 0 8 is close to 1 past returns seem to be a very good positive predictor of future returns for this particular stock therefore rain can adjust their portfolio to take advantage of the autocorrelation or momentum by continuing to hold their position or accumulating more shares 