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Extending the feature set of a data-driven artificial neural network model of pricing financial option

Record type: Conference or Workshop Item (Paper)

Prices of derivative contracts, such as options, traded in the financial markets are expected to have complex relationships to fluctuations in the values of the underlying assets, the time to maturity and type of exercise of the contracts as well as other macroeconomic variables. Hutchinson, Lo and Poggio showed in 1994 that a non-parametric artificial neural network may be trained to approximate this complex functional relationship. Here, we consider this model with additional inputs relevant to the pricing of options and show
that the accuracy of approximation may indeed be improved. We consider volume traded, historic volatility, observed interest rates and combinations of these as additional features. In addition to giving empirical results on how the inclusion of these variables helps predicting option prices, we also analyse prediction errors of the different models with volatility and volume traded as inputs, and report an interesting correlation between their contributions.

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Citation

Montesdeoca Bermudez, Luis and Niranjan, Mahesan (2016) Extending the feature set of a data-driven artificial neural network model of pricing financial option At 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Greece. 06 - 09 Dec 2016. 6 pp.

More information

Accepted/In Press date: 12 September 2016
e-pub ahead of print date: 8 December 2016
Venue - Dates: 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Greece, 2016-12-06 - 2016-12-09
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 404353
URI: http://eprints.soton.ac.uk/id/eprint/404353
PURE UUID: 8941cac0-ea81-4b4e-85b8-a1d092e8b901

Catalogue record

Date deposited: 10 Jan 2017 09:44
Last modified: 17 Jul 2017 17:34

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Contributors

Author: Luis Montesdeoca Bermudez

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