loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Author: A. J. Hoffman

Affiliation: North-West University, South Africa

ISBN: 978-989-758-054-3

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. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.205.60.226

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Hoffman, A. (2014). Combining Different Computational Techniques in the Development of Financial Prediction Models.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 276-281. DOI: 10.5220/0005136502760281

@conference{ncta14,
author={A. J. Hoffman.},
title={Combining Different Computational Techniques in the Development of Financial Prediction Models},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={276-281},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005136502760281},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Combining Different Computational Techniques in the Development of Financial Prediction Models
SN - 978-989-758-054-3
AU - Hoffman, A.
PY - 2014
SP - 276
EP - 281
DO - 10.5220/0005136502760281

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.