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intelligent neural network forecaster to predict the Standard & Poor 500's index
- Date Issued:
- 1999
- Summary:
- In this thesis we present an intelligent forecaster based on neural network technology to capture the future path of the market indicator. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using the financial indicators as the input variables. A complex recurrent neural network is used to capture the behavior of the nonlinear characteristics of the S&P 500. The main outcome of this research is, a systematic way of constructing a forecaster for nonlinear and non-stationary data series of S&P 500 that leads to very good out-of-sample prediction. The results of the training and testing of the network are presented along with conclusion. The tool used for the validation of this research is "Brainmaker". This thesis also contains a brief survey of available tools for financial forecasting.
Title: | An intelligent neural network forecaster to predict the Standard & Poor 500's index. |
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Name(s): |
Shah, Sulay Bipin. Florida Atlantic University, Degree grantor Pandya, Abhijit S., Thesis advisor College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Issuance: | monographic | |
Date Issued: | 1999 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 159 p. | |
Language(s): | English | |
Summary: | In this thesis we present an intelligent forecaster based on neural network technology to capture the future path of the market indicator. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using the financial indicators as the input variables. A complex recurrent neural network is used to capture the behavior of the nonlinear characteristics of the S&P 500. The main outcome of this research is, a systematic way of constructing a forecaster for nonlinear and non-stationary data series of S&P 500 that leads to very good out-of-sample prediction. The results of the training and testing of the network are presented along with conclusion. The tool used for the validation of this research is "Brainmaker". This thesis also contains a brief survey of available tools for financial forecasting. | |
Identifier: | 9780599540705 (isbn), 15741 (digitool), FADT15741 (IID), fau:12496 (fedora) | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): |
College of Engineering and Computer Science Thesis (M.S.)--Florida Atlantic University, 1999. |
|
Subject(s): |
Neural networks (Computer science) Stock price forecasting Time-series analysis |
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Held by: | Florida Atlantic University Libraries | |
Persistent Link to This Record: | http://purl.flvc.org/fcla/dt/15741 | |
Sublocation: | Digital Library | |
Use and Reproduction: | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU | |
Is Part of Series: | Florida Atlantic University Digital Library Collections. |