A stochastic process used
in statistical calculations in which future values are estimated
based on a weighted sum of past values. An autoregressive process
operates under the premise that past values have an effect on current
values. A process considered AR(1) is the first order process,
meaning that the current value is based on the immediately preceding
value. An AR(2) process has the current value based on the previous
two values.
Autoregressive processes are used by investors in technical analysis. Trends, moving averages and regressions take into account past prices in an effort to create forecasts of future price movement. One drawback to this type of analysis is that past prices won't always be the best predictor of future movements, especially if the underlying fundamentals of a company have changed.
Autoregressive processes are used by investors in technical analysis. Trends, moving averages and regressions take into account past prices in an effort to create forecasts of future price movement. One drawback to this type of analysis is that past prices won't always be the best predictor of future movements, especially if the underlying fundamentals of a company have changed.
