Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction

Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction

David R. Selviah (University College London, UK) and Janti Shawash (University College London, UK)
Copyright: © 2009 |Pages: 38
DOI: 10.4018/978-1-59904-897-0.ch010
OnDemand PDF Download:
$37.50

Abstract

Generalized correlation higher order neural network designs are developed. Their performance is compared with that of first order networks, conventional higher order neural network designs, and higher order linear regression networks for financial time series prediction. The correlation higher order neural network design is shown to give the highest accuracy for prediction of stock market share prices and share indices. The simulations compare the performance for three different training algorithms, stationary versus non-stationary input data, different numbers of neurons in the hidden layer and several generalized correlation higher order neural network designs. Generalized correlation higher order linear regression networks are also introduced and two designs are shown by simulation to give good correct direction prediction and higher prediction accuracies, particularly for long-term predictions, than other linear regression networks for the prediction of inter-bank lending risk Libor and Swap interest rate yield curves. The simulations compare the performance for different input data sample lag lengths.
Chapter Preview
Top

Extended Correlation Model Of Neural Networks

The theoretical derivation below is carried out using continuous functions and integrals, for convenience, and then digitized into numerical form with summations although it could also be all carried out in digital form.

Figure

1 Correlator model of one neural network layer

Complete Chapter List

Search this Book:
Reset