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An adaptive NARX neural network approach for financial time series prediction
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Title
An adaptive NARX neural network approach for financial time series prediction
Name
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Soman
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Parashar Chandrashekhar
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Parashar Chandrashekhar Soman
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author
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Marsic
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Ivan
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Advisory Committee
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Ivan Marsic
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chair
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Zoran
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Advisory Committee
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Zoran Gajic
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internal member
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Parashar
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Manish
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Advisory Committee
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Manish Parashar
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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theses
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2008
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2008-10
Language
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English
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electronic
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vii, 104 pages
Abstract
There has been increasing interest in the application of neural networks to the field of finance. Several experiments have been carried out stating the success of neural networks for time series prediction.
Most of the existing systems recommend single neural network architecture to be used for a particular time series. Our experiments have shown that a fixed architecture may not be the best approach across different time horizons. The thesis proposes a new methodology where multiple NARX (nonlinear autoregressive network with exogenous inputs) networks with different architectures are generated and evaluated before the beginning of a new time horizon. A network is selected from this set and employed to make predictions. This selection is based on past datasets only -- making the system completely applicable to real world scenarios.
A framework of functions was built in MATLAB® to customize the Neural Network Toolbox ® for financial applications. This framework provides for all the basic functions required by a financial neural network system. An adaptive system that uses technical indicators and some external time series as inputs was built. Different rules were developed and tested for selecting the best performing neural networks.
The new approach was tested on 5 currencies and the gold series. Our results show that high realized values of returns in the past, along with generalization is the best parameter to select a network for the future. A system with adaptive approach performs better than one with a fixed architecture. Our adaptive system out performed not only the fixed architectures but also other benchmarks like technical indicators, linear regression and baseline buy or sell strategies.
Most of the existing systems recommend single neural network architecture to be used for a particular time series. Our experiments have shown that a fixed architecture may not be the best approach across different time horizons. The thesis proposes a new methodology where multiple NARX (nonlinear autoregressive network with exogenous inputs) networks with different architectures are generated and evaluated before the beginning of a new time horizon. A network is selected from this set and employed to make predictions. This selection is based on past datasets only -- making the system completely applicable to real world scenarios.
A framework of functions was built in MATLAB® to customize the Neural Network Toolbox ® for financial applications. This framework provides for all the basic functions required by a financial neural network system. An adaptive system that uses technical indicators and some external time series as inputs was built. Different rules were developed and tested for selecting the best performing neural networks.
The new approach was tested on 5 currencies and the gold series. Our results show that high realized values of returns in the past, along with generalization is the best parameter to select a network for the future. A system with adaptive approach performs better than one with a fixed architecture. Our adaptive system out performed not only the fixed architectures but also other benchmarks like technical indicators, linear regression and baseline buy or sell strategies.
Note
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M.S.
Note
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Includes bibliographical references (p. 80-82).
Subject
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Topic
Electrical and Computer Engineering
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Topic
Future market--Forecasting--Mathematical models
Subject
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Topic
Time-series analysis--Mathematical models
Subject
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Topic
Neural networks (Computer science)
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17575
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ETD_1130
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doi:10.7282/T3474B5N
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ETD graduate
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Rights
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Copyright protected
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Open
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Parashar Soman
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Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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