Article

A neural evolutionary approach to financial modeling

Authors:

Antonia Azzini,

Andrea G.B. TettamanziAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 1605 - 1612

https://doi.org/10.1145/1143997.1144263

Published: 08 July 2006 Publication History

Get Access

Abstract

This paper presents an approach to the joint optimization of neural network structure and weights which can take advantage of backpropagation as a specialized decoder. The approach has been applied to a financial problem, whereby a factor model capturing the mutual relationships among several financial instruments is sought for. A sample application of such a model to statistical arbitrage is also presented.

References

[1]

P. A. Castillo, J. Carpio, J. J. Merelo, A. Prieto, V. Rivas, and G. Romero. Evolving multilayer perceptrons. Neural Processing Letters, 12(2):115--127, 2000.]]

Digital Library

Google Scholar

[2]

L. Harris. Trading and exchanges: market microstructure for practitioners. Oxford University Press, Oxford, 2003.]]

Google Scholar

[3]

R. Keesing and D. Stork. Evolution and learning in neural networks: the number and distribution of learning trials affect the rate of evolution. Advanced in Neural Information Processing Systems, 3:805--810, 1991.]]

Digital Library

Google Scholar

[4]

V. Maniezzo. Genetic evolution fo the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks, 5(1):39--53, January 1994.]]

Digital Library

Google Scholar

[5]

G. Miller, P. Todd, and S. Hegde. Designing neural networks using genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms, pages 379--384. J.D. Schaffer, 1989.]]

Digital Library

Google Scholar

[6]

D. Montana and L. Davis. Training feedforward neural networks using genetic algorithms. In Proceedings of the 11th International Conference on Artificial Intelligence, pages 762--767. Morgan Kaufmann, 1989.]]

Google Scholar

[7]

D. E. Rumelhart, J. L. McClelland, and R. J. Williams. Learning representations by back-propagating errors. Nature, 323:533--536, 1986.]]

Crossref

Google Scholar

[8]

K. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Technical report, University of Texas at Austin, Department of Computer Science, Austin, Texas, 2001.]]

Digital Library

Google Scholar

[9]

D. Whitley and T. Hanson. Optimizing neural networks using faster, more accurate genetic search. In Proceedings of the Third International Conference on Genetic Algorithms, pages 391--396. J. D. Schaffer, 1989.]]

Digital Library

Google Scholar

[10]

D. Whitley, T. Starkweather, and C. Bogart. Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel computing, 14:347--361, 1993.]]

Crossref

Google Scholar

[11]

H. Muhlenbein and D. Schlierkamp-Voosen. The science of breeding and its application to the breeder genetic algorithm (bga). Evolutionary Computation, 1(4):335--360, 1993.]]

Digital Library

Google Scholar

[12]

X. Yao. Evolving artificial neural networks. In Proceedings on IEEE, volume 87, pages 1423--1447, 1999.]]

Google Scholar

[13]

X. Yao and Y.Liu. A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks, 8(3):694--713, May 1997.]]

Digital Library

Google Scholar

[14]

X. Yao and Y.Liu. Towards designing artificial neural networks by evolution. Applied Mathematics and Computation, 91(1):83--90, 1998.]]

Digital Library

Google Scholar

Cited By

View all

Pal ASaha DDash NNaskar SPal A(2019)A novel approach to word sense disambiguation in Bengali language using supervised methodologySādhanā10.1007/s12046-019-1165-244:8Online publication date: 17-Jul-2019
https://doi.org/10.1007/s12046-019-1165-2
Aranha CAzevedo CIba H(2012)Money in treesInformation Sciences: an International Journal10.1016/j.ins.2011.05.023182:1(184-198)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1016/j.ins.2011.05.023
Zhai FLin XYang ZSong Y(2010)Notice of Retraction: Financial time series prediction based on Echo State Network2010 Sixth International Conference on Natural Computation10.1109/ICNC.2010.5584802(3983-3987)Online publication date: Aug-2010
https://doi.org/10.1109/ICNC.2010.5584802
Show More Cited By

Index Terms

A neural evolutionary approach to financial modeling
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

Recommendations

A neuro-evolutionary approach to intraday financial modeling
EvoApplications'12: Proceedings of the 2012t European conference on Applications of Evolutionary Computation

We investigate the correlations among the intraday prices of the major stocks of the Milan Stock Exchange by means of a neuro-evolutionary modeling method. In particular, the method used to approach such problem is to apply a very powerful natural ...
Automated trading on financial instruments with evolved neural networks
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

This paper presents an approach to single-position, intraday automated trading based on a neuro-genetic algorithm.An artificial neural network is evolved which provides trading signals to a very unsophisticated automated trading agent.
Evolutionary training of deep neural networks on heterogeneous computing environments
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Deep neural networks are typically trained using gradient-based optimizers such as error backpropagation. This study proposes a framework based on Evolutionary Algorithms (EAs) to train deep neural networks without gradients. The network parameters, ...

Comments

Information & Contributors

Information

Published In

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

GECCO06

Sponsor:

GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

Acceptance Rates

GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
20
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Pal ASaha DDash NNaskar SPal A(2019)A novel approach to word sense disambiguation in Bengali language using supervised methodologySādhanā10.1007/s12046-019-1165-244:8Online publication date: 17-Jul-2019
https://doi.org/10.1007/s12046-019-1165-2
Aranha CAzevedo CIba H(2012)Money in treesInformation Sciences: an International Journal10.1016/j.ins.2011.05.023182:1(184-198)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1016/j.ins.2011.05.023
Zhai FLin XYang ZSong Y(2010)Notice of Retraction: Financial time series prediction based on Echo State Network2010 Sixth International Conference on Natural Computation10.1109/ICNC.2010.5584802(3983-3987)Online publication date: Aug-2010
https://doi.org/10.1109/ICNC.2010.5584802
Azzini Ada Costa Pereira CTettamanzi A(2010)Modeling Turning Points in Financial Markets with Soft Computing TechniquesNatural Computing in Computational Finance10.1007/978-3-642-13950-5_9(147-167)Online publication date: 2010
https://doi.org/10.1007/978-3-642-13950-5_9
Smith RBehzadan B(2009)Mutual information neuro-evolutionary system (MINES)Proceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689800(1523-1529)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689800
Butler MDaniyal ARothlauf F(2009)Multi-objective optimization with an evolutionary artificial neural network for financial forecastingProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570096(1451-1458)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1570096
Ghandar AMichalewicz ZSchmidt MTô TZurbrugg R(2009)Computational intelligence for evolving trading rulesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2008.91599213:1(71-86)Online publication date: 1-Feb-2009
https://dl.acm.org/doi/10.1109/TEVC.2008.915992
Smith RBehzadan B(2009)Mutual information neuro-evolutionary system (MINES)2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983123(1523-1529)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983123
Butler MKešelj V(2009)Optimizing a Pseudo Financial Factor Model with Support Vector Machines and Genetic ProgrammingProceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence10.1007/978-3-642-01818-3_21(191-194)Online publication date: 15-May-2009
https://dl.acm.org/doi/10.1007/978-3-642-01818-3_21
Azzini ATettamanzi A(2008)Evolutionary single-position automated tradingProceedings of the 2008 conference on Applications of evolutionary computing10.5555/1787943.1787952(62-72)Online publication date: 26-Mar-2008
https://dl.acm.org/doi/10.5555/1787943.1787952
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

A neuro-evolutionary approach to intraday financial modeling

Automated trading on financial instruments with evolved neural networks

Evolutionary training of deep neural networks on heterogeneous computing environments

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations