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Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior

Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior

Sarat Chandra Nayak, Bijan Bihari Misra, Himansu Sekhar Behera
Copyright: © 2016 |Pages: 18
ISBN13: 9781522500636|ISBN10: 1522500634|EISBN13: 9781522500643
DOI: 10.4018/978-1-5225-0063-6.ch007
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MLA

Nayak, Sarat Chandra, et al. "Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior." Applied Artificial Higher Order Neural Networks for Control and Recognition, edited by Ming Zhang, IGI Global, 2016, pp. 174-191. https://doi.org/10.4018/978-1-5225-0063-6.ch007

APA

Nayak, S. C., Misra, B. B., & Behera, H. S. (2016). Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior. In M. Zhang (Ed.), Applied Artificial Higher Order Neural Networks for Control and Recognition (pp. 174-191). IGI Global. https://doi.org/10.4018/978-1-5225-0063-6.ch007

Chicago

Nayak, Sarat Chandra, Bijan Bihari Misra, and Himansu Sekhar Behera. "Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior." In Applied Artificial Higher Order Neural Networks for Control and Recognition, edited by Ming Zhang, 174-191. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0063-6.ch007

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Abstract

This chapter presents two higher order neural networks (HONN) for efficient prediction of stock market behavior. The models include Pi-Sigma, and Sigma-Pi higher order neural network models. Along with the traditional gradient descent learning, how the evolutionary computation technique such as genetic algorithm (GA) can be used effectively for the learning process is also discussed here. The learning process is made adaptive to handle the noise and uncertainties associated with stock market data. Further, different prediction approaches are discussed here and application of HONN for time series forecasting is illustrated with real life data taken from a number of stock markets across the globe.

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