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new methodology to predict certain characteristics of stock market using time-series phenomena

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Date Issued:
1999
Summary:
The goal of time series forecasting is to identify the underlying pattern and use these patterns to predict the future path of the series. To capture the future path of a dynamic stock market variable is one of the toughest challenges. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using time-series phenomena. The main outcome of this new approach for financial forecasting is a systematic way of constructing a Neural Network Forecaster for nonlinear and non-stationary time-series data that leads to very good out-of-sample prediction. The tool used for the validation of this research is "Brainmaker". This thesis also contains a small survey of available tools used for financial forecasting.
Title: A new methodology to predict certain characteristics of stock market using time-series phenomena.
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Name(s): Shah, Trupti U.
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
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: 140 p.
Language(s): English
Summary: The goal of time series forecasting is to identify the underlying pattern and use these patterns to predict the future path of the series. To capture the future path of a dynamic stock market variable is one of the toughest challenges. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using time-series phenomena. The main outcome of this new approach for financial forecasting is a systematic way of constructing a Neural Network Forecaster for nonlinear and non-stationary time-series data that leads to very good out-of-sample prediction. The tool used for the validation of this research is "Brainmaker". This thesis also contains a small survey of available tools used for financial forecasting.
Identifier: 9780599375192 (isbn), 15677 (digitool), FADT15677 (IID), fau:12433 (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): Time-series analysis
Neural networks (Computer science)
Stock price forecasting
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/15677
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.