A comparative survey of three AI techniques (NN, PSO, and GA) in financial domain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In the real world, the behaviors of financial applications are unstable and they change from time to time. Accordingly, dealing with such issues as nonlinear and time variant problems has been a serious problem in recent years. These types of problems along with inefficiency of the traditional models led to an increasing interest in artificial intelligence approaches. In this study, we briefly review three popular artificial intelligence methods, i.e., Artificial Neural Networks, Genetic Algorithms, and Particle Swarm Optimization, and compare their applications in financial domain. By considering the broad domain of financial applications, we classify financial market into three domains, including financial forecasting, credit evaluation, and portfolio management. For each technique, we have attempted to take the most recent and popular studies into account. The results are promising and represent that in handling financial problems, the performance and accuracy of the above mentioned artificial intelligence techniques are considerably higher, compared to the traditional statistical techniques, particularly in nonlinear models. Nevertheless, this superiority is not true in all cases.

Original languageEnglish
Title of host publicationProceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
Pages332-337
Number of pages6
Publication statusPublished - 2012
Event2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012 - Seoul
Duration: 3 Dec 20125 Dec 2012

Other

Other2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012
CitySeoul
Period3/12/125/12/12

Fingerprint

Particle swarm optimization (PSO)
Artificial intelligence
Genetic algorithms
Neural networks
Financial markets

Keywords

  • Artificial neural networks
  • Credit evaluation
  • Financial prediction and planning
  • Genetic Algorithms
  • particle Swarm Optimization
  • Portfolio management

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this

Beiranvand, V., Abu Bakar, A., & Othman, Z. (2012). A comparative survey of three AI techniques (NN, PSO, and GA) in financial domain. In Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012 (pp. 332-337). [6530353]

A comparative survey of three AI techniques (NN, PSO, and GA) in financial domain. / Beiranvand, Vahid; Abu Bakar, Azuraliza; Othman, Zalinda.

Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012. 2012. p. 332-337 6530353.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Beiranvand, V, Abu Bakar, A & Othman, Z 2012, A comparative survey of three AI techniques (NN, PSO, and GA) in financial domain. in Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012., 6530353, pp. 332-337, 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012, Seoul, 3/12/12.
Beiranvand V, Abu Bakar A, Othman Z. A comparative survey of three AI techniques (NN, PSO, and GA) in financial domain. In Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012. 2012. p. 332-337. 6530353
Beiranvand, Vahid ; Abu Bakar, Azuraliza ; Othman, Zalinda. / A comparative survey of three AI techniques (NN, PSO, and GA) in financial domain. Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012. 2012. pp. 332-337
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