Improving Artificial Neural Network Forecasts with Kalman Filtering

Abstract

In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies over a period of 750 trading days. In all the cases we find that the Kalman filter algorithm significantly adds value to the forecasting process.

Keywords: Artificial Neural Networks, Kalman filter, Stock prices, Forecasting, Back propagation