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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, Janti Shawash
Copyright: © 2009 |Pages: 38
ISBN13: 9781599048970|ISBN10: 1599048973|ISBN13 Softcover: 9781616925673|EISBN13: 9781599048987
DOI: 10.4018/978-1-59904-897-0.ch010
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MLA

Selviah, David R., and Janti Shawash. "Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction." Artificial Higher Order Neural Networks for Economics and Business, edited by Ming Zhang, IGI Global, 2009, pp. 212-249. https://doi.org/10.4018/978-1-59904-897-0.ch010

APA

Selviah, D. R. & Shawash, J. (2009). Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction. In M. Zhang (Ed.), Artificial Higher Order Neural Networks for Economics and Business (pp. 212-249). IGI Global. https://doi.org/10.4018/978-1-59904-897-0.ch010

Chicago

Selviah, David R., and Janti Shawash. "Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction." In Artificial Higher Order Neural Networks for Economics and Business, edited by Ming Zhang, 212-249. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-897-0.ch010

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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.

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