Author:
A. J. Hoffman
Affiliation:
North-West University, South Africa
Keyword(s):
Neural Networks, Linear Regression, Histograms, Financial Time Series, Prediction, Portfolio Management.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Based Data Mining and Complex Information Processing
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The prediction of financial time series to enable improved portfolio management is a complex topic that has been widely researched. Modelling challenges include the high level of noise present in the signals, the need to accurately model extreme rather than average behaviour, the inherent non-linearity of relationships between explanatory and predicted variables and the need to predict the future behaviour of a large number of independent investment instruments that must be considered for inclusion into a well-diversified portfolio. This paper demonstrates that linear time series prediction does not offer the ability to develop reliable prediction models, due to the inherently non-linear nature of the relationship between explanatory and predicted variables. It is shown that the results of histogram based sorting techniques can be used to guide the selection of suitable variables to be included in the development of a neural network model. We find that multivariate neural network
models can outperform the best models using only a single explanatory variable. We furthermore demonstrate that the stochastic nature of the signals can be addressed by training common models for a number of similar instruments which forces the neural network to model the underlying relationships rather than the noise in the signals.
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