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Artificial Neural Networks in Financial Trading

Artificial Neural Networks in Financial Trading

Bruce Vanstone, Clarence Tan
Copyright: © 2008 |Pages: 7
ISBN13: 9781599049410|ISBN10: 1599049414|EISBN13: 9781599049427
DOI: 10.4018/978-1-59904-941-0.ch099
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MLA

Vanstone, Bruce, and Clarence Tan. "Artificial Neural Networks in Financial Trading." Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications, edited by Vijayan Sugumaran, IGI Global, 2008, pp. 1758-1764. https://doi.org/10.4018/978-1-59904-941-0.ch099

APA

Vanstone, B. & Tan, C. (2008). Artificial Neural Networks in Financial Trading. In V. Sugumaran (Ed.), Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications (pp. 1758-1764). IGI Global. https://doi.org/10.4018/978-1-59904-941-0.ch099

Chicago

Vanstone, Bruce, and Clarence Tan. "Artificial Neural Networks in Financial Trading." In Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications, edited by Vijayan Sugumaran, 1758-1764. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-941-0.ch099

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Abstract

Soft computing represents that area of computing adapted from the physical sciences. Artificial intelligence (AI) techniques within this realm attempt to solve problems by applying physical laws and processes. This style of computing is particularly tolerant of imprecision and uncertainty, making the approach attractive to those researching within “noisy” realms, where the signal-to-noise ratio is low. Soft computing is normally accepted to include the three key areas of fuzzy logic, artificial neural networks, and probabilistic reasoning (which includes genetic algorithms, chaos theory, etc.).

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