what is an autoregressive model 
a statistical model is autoregressive if it predicts future values based on past values for example an autoregressive model might seek to predict a stock s future prices based on its past performance understanding autoregressive modelsautoregressive models operate under the premise that past values have an effect on current values which makes the statistical technique popular for analyzing nature economics and other processes that vary over time multiple regression models forecast a variable using a linear combination of predictors whereas autoregressive models use a combination of past values of the variable an ar 1 autoregressive process is one in which the current value is based on the immediately preceding value while an ar 2 process is one in which the current value is based on the previous two values an ar 0 process is used for white noise and has no dependence between the terms in addition to these variations there are also many different ways to calculate the coefficients used in these calculations such as the least squares method these concepts and techniques are used by technical analysts to forecast security prices however since autoregressive models base their predictions only on past information they implicitly assume that the fundamental forces that influenced the past prices will not change over time this can lead to surprising and inaccurate predictions if the underlying forces in question are in fact changing such as if an industry is undergoing rapid and unprecedented technological transformation nevertheless traders continue to refine the use of autoregressive models for forecasting purposes a great example is the autoregressive integrated moving average arima a sophisticated autoregressive model that can take into account trends cycles seasonality errors and other non static types of data when making forecasts although autoregressive models are associated with technical analysis they can also be combined with other approaches to investing for example investors can use fundamental analysis to identify a compelling opportunity and then use technical analysis to identify entry and exit points example of an autoregressive modelautoregressive models are based on the assumption that past values have an effect on current values for example an investor using an autoregressive model to forecast stock prices would need to assume that new buyers and sellers of that stock are influenced by recent market transactions when deciding how much to offer or accept for the security although this assumption will hold under most circumstances this is not always the case for example in the years prior to the 2008 financial crisis most investors were not aware of the risks posed by the large portfolios of mortgage backed securities held by many financial firms during those times an investor using an autoregressive model to predict the performance of u s financial stocks would have had good reason to predict an ongoing trend of stable or rising stock prices in that sector however once it became public knowledge that many financial institutions were at risk of imminent collapse investors suddenly became less concerned with these stocks recent prices and far more concerned with their underlying risk exposure therefore the market rapidly revalued financial stocks to a much lower level a move which would have utterly confounded an autoregressive model it is important to note that in an autoregressive model a one time shock will affect the values of the calculated variables infinitely into the future therefore the legacy of the financial crisis lives on in today s autoregressive models investopedia does not provide tax investment or financial services and advice the information is presented without consideration of the investment objectives risk tolerance or financial circumstances of any specific investor and might not be suitable for all investors investing involves risk including the possible loss of principal 