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Enhancing intraday trading performance of Neural Network using dynamic volatility clustering fuzzy filter

Vella, Vince and Ng, Wing Lon (2014) Enhancing intraday trading performance of Neural Network using dynamic volatility clustering fuzzy filter. In: 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 27-28 March 2014, London.

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Abstract

We extend Neural Network (NN) trading models with an innovative and efficient volatility filter based on fuzzy c-means clustering algorithm, where the choice for the number of clusters, a frequent problem with cluster analysis, is selected by optimizing a global risk-return performance measure. Our algorithm automatically extracts fuzzy rules from past trades by taking into account the predicted return size and intraday time varying realized volatility, the latter used as a proxy for uncertainty. The model identifies unique intraday scenarios and subsequently creates a dynamic and visually apprehensible risk-return search space to control algorithmic trading decisions. Our results show that this method can be successfully applied to support high-frequency intraday trading strategies, outperforming both standard NN and buy-and-hold models.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of > Centre for Computational Finance and Economic Agents
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 19 Jul 2015 16:42
Last Modified: 19 Jul 2015 16:42
URI: http://repository.essex.ac.uk/id/eprint/14337

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