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Neural networks for large financial crashes forecast

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Title: Neural networks for large financial crashes forecast
Authors: Rotundo, G.1 giulia.rotundo@uniroma1.it
Source: Physica A. Dec2004, Vol. 344 Issue 1/2, p77-80. 4p.
Subjects: Business forecasting, Biological neural networks, Cognitive neuroscience, Differential equations
Abstract: Abstract: The aim of this work is to examine how neural networks can be used for solving the problem of the forecast of large financial crashes due to the presence of speculative bubbles. Some microeconomic theories have been developed for the explanation of a bubble due to a cooperation among the investors. This behaviour can be detected by the presence of self-similarity in the indexes series near the crash time leading to a differential equation and thus to a dynamical system description, well suitable by a neural network approach. [Copyright &y& Elsevier]
Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Abstract: The aim of this work is to examine how neural networks can be used for solving the problem of the forecast of large financial crashes due to the presence of speculative bubbles. Some microeconomic theories have been developed for the explanation of a bubble due to a cooperation among the investors. This behaviour can be detected by the presence of self-similarity in the indexes series near the crash time leading to a differential equation and thus to a dynamical system description, well suitable by a neural network approach. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.physa.2004.06.091
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      – SubjectFull: Cognitive neuroscience
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              Text: Dec2004
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