Title of article :
Linear and nonlinear dynamic systems in financial time series prediction
Author/Authors :
Lahmiri، Salim نويسنده ,
Issue Information :
ماهنامه با شماره پیاپی 11 سال 2012
Pages :
6
From page :
2551
To page :
2556
Abstract :
Autoregressive moving average (ARMA) process and dynamic neural networks namely the nonlinear autoregressive moving average with exogenous inputs (NARX) are compared by evaluating their ability to predict financial time series; for instance the S&P500 returns. Two classes of ARMA are considered. The first one is the standard ARMA model which is a linear static system. The second one uses Kalman filter (KF) to estimate and predict ARMA coefficients. This model is a linear dynamic system. The forecasting ability of each system is evaluated by means of mean absolute error (MAE) and mean absolute deviation (MAD) statistics. Simulation results indicate that the ARMA-KF system performs better than the standard ARMA alone. Thus, introducing dynamics into the ARMA process improves the forecasting accuracy. In addition, the ARMA-KF outperformed the NARX. This result may suggest that the linear component found in the S&P500 return series is more dominant than the nonlinear part. In sum, we conclude that introducing dynamics into the ARMA process provides an effective system for S&P500 time series prediction.
Journal title :
Management Science Letters
Serial Year :
2012
Journal title :
Management Science Letters
Record number :
680595
Link To Document :
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