Neural network applications in finance: A review and analysis of literature (1990–1996)

Wong, Bo K.; Selvi, Yakup

doi:10.1016/s0378-7206(98)00050-0

Cited by 184 publications

(57 citation statements)

References 63 publications

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“…Furthermore, several different methods have been used for financial forecasting. Some examples of such methods are Support Vector Machines, [8][9][10] Artificial Neural Networks, [11][12][13][14] and Genetic Programming. 15,16 Furthermore, some of these methods have been combined to produce hybrid systems.…”

Section: Introductionmentioning

confidence: 99%

Investment Opportunities Forecasting: Extending the Grammar of a GP-based Tool

Kampouridis¹,

Tsang²

2012

IJCIS

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In this paper we present a new version of a GP financial forecasting tool, called EDDIE 8. The novelty of this version is that it allows the GP to search in the space of indicators, instead of using pre-specified ones. We compare EDDIE 8 with its predecessor, EDDIE 7, and find that new and improved solutions can be found. Analysis also shows that, on average, EDDIE 8's best tree performs better than the one of EDDIE 7. The above allows us to characterize EDDIE 8 as a valuable forecasting tool.

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Section: Introductionmentioning

confidence: 99%

Investment Opportunities Forecasting: Extending the Grammar of a GP-based Tool

Kampouridis¹,

Tsang²

2012

IJCIS

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“…The full-text of each of the 390 articles was reviewed by the authors and classified according to categories suggested by previous studies [277,398]. The articles were classified and coded according to:…”

Section: Classification Methodsmentioning

confidence: 99%

Enterprise Adoption of ICT Innovations: Multi-disciplinary Literature Analysis and Future Research Opportunities

Basole

2008

Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008)

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“…One family of NC methods which has seen extensive application for financial forecasting is NNs (e.g., [121,56,2,17,42]). They offer particular advantages due to their ability to identify non-linear models, handle noisy data and embed a memory (recurrent NNs).…”

Section: Forecastingmentioning

confidence: 99%