Authors:
Rosheena Siddiqi
and
Syed Nasir Danial
Affiliation:
Bahria University, Pakistan
Keyword(s):
Financial forecasting, Bicorrelation test, Transient nonlinearity, Neural network.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Enterprise Information Systems
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper employs the Hinich portmanteau bicorrelation test with the windowed testing method to identify nonlinear behavior in the rate of returns series for Karachi Stock Exchange indices. The stock returns series can be described to be comprising of few brief phases of highly significant nonlinearity, followed by long phases in which the returns follow a pure noise process. It has been identified that major political and economic events have contributed to the short bursts of nonlinear behavior in the returns series. Finally, these periods of nonlinear behavior are used to predict the behavior of the rest of the regions using a feedforward neural network and dynamic neural network with Bayesian Regularization Learning. The dynamic neural network outperforms the traditional feedforward networks because Bayesian regularization learning method is used to reduce the training epochs. The time-series generating process is found to closely resemble a white noise process with weak depende
nce on value at lag one.
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