Serial Correlation

DEFINITION of 'Serial Correlation'

Serial correlation is the relationship between a given variable and itself over various time intervals. Serial correlations are often found in repeating patterns, when the level of a variable effects 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.

BREAKING DOWN 'Serial Correlation'

The term serial correlation can also be referred to as "autocorrelation" or "lagged 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 toward 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

The idea behind serial correlation is that it was originally used in engineering to determine how a signal, like a computer signal or radio wave, varies with itself over time. It started to catch on in economic circles as economists and partitioners 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. These 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.