Combining Different Computational Techniques in the Development of Financial Prediction Models

A. J. Hoffman

2014

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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