Serial Correlation

DEFINITION of 'Serial Correlation'

Serial correlation is the relationship between a given variable and a lagged version of itself over various time intervals. Serial correlations are often found in repeating patterns, when the level of a variable affects its future level. In finance, this correlation is used by technical analysts to determine how well the past price of a security predicts the future price.

Serial correlation is also known as autocorrelation or lagged correlation.

BREAKING DOWN 'Serial Correlation'

Serial correlation is a term used in statistics to describe the relationship between observations of the same variable over specific periods of time. If a variable's serial correlation is measured to be zero, then it means there is no correlation, and each of the observations are independent of one another. Conversely, if a variable's serial correlation skews towards one, it means that the observations are serially correlated, and that future observations are affected by past values. Essentially, a variable that is serially correlated has a pattern and isn't random.

Measures of serial correlation are used in technical analysis when analyzing a security's pattern. The analysis is based entirely on a stock's price movement and the associated volume, rather than a company's fundamentals. Practitioners of technical analysis, if they use serial correlation correctly, are able to find and validate the profitable patterns or a security or group of securities, and spot investment opportunities.

The Concept of Serial Correlation

Serial correlation was originally used in engineering to determine how a signal, such as a computer signal or radio wave, varies with itself over time. It started to catch on in economic circles as economists and practitioners of econometrics used it to analyze economic data over time. These academics began to leave academia in search of Wall Street, and by the 1980s, the use of serial correlation was being used to predict stock prices.

Almost all large financial institutions now have quantitative analysts, known as "quants," on staff. These financial trading analysts use technical analysis and other statistical inferences to analyze and predict the stock market. Quants are integral to the success of many of these financial institutions, since they are relied on to provide market models that the institution then uses as the bases for its investment strategy.

Serial correlation among these quants is determined using the Durbin-Watson test. The correlation can be either positive or negative. A stock price displaying positive serial correlation, as one would guess, means that the correlation has a positive pattern. A security that has a negative serial correlation, on the other hand, has a negative influence on itself over time.