Artificial Higher Order Neural Networks for Economics and Business

542 Pages·2008·9.06 MB·English

by  Zhang, Ming

Artificial Higher Order
Neural Networks for
Economics and Business
Ming Zhang
Christopher Newport University, USA
InformatIon scIence reference
Hershey • New York
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Library of Congress Cataloging-in-Publication Data
Artificial higher order neural networks for economics and business / Ming Zhang, editor.
p. cm.
Summary: “This book is the first book to provide opportunities for millions working in economics, accounting, finance and other
business areas education on HONNs, the ease of their usage, and directions on how to obtain more accurate application results. It provides
significant, informative advancements in the subject and introduces the HONN group models and adaptive HONNs”--Provided by publisher.
ISBN-13: 978-1-59904-897-0 (hbk.)
ISBN-13: 978-1-59904-898-7 (e-book)
1. Finance--Computer simulation. 2. Finance--Mathematical models. 3. Finance--Computer programs. 4. Neural networks (Computer
science) I. Zhang, Ming, 1949 July 29-
HG106.A78 2008
332.0285’632--dc22
2007043953
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book set is original material. The views expressed in this book are those of the authors, but not necessarily of
the publisher.
If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating
the library's complimentary electronic access to this publication.
To My Wife,
Zhao Qing Zhang
Table of Contents
Preface ...............................................................................................................................................xvii
Acknowledgment .............................................................................................................................xxiii
Section I
Artificial Higher Order Neural Networks for Economics
Chapter I
Artificial Higher Order Neural Network Nonlinear Models: SAS NLIN or HONNs? ..........................1
Ming Zhang, Christopher Newport University, USA
Chapter II
Higher Order Neural Networks with Bayesian Confidence Measure for the Prediction
of the EUR/USD Exchange Rate .........................................................................................................48
Adam Knowles, Liverpool John Moores University, UK
Abir Hussain, Liverpool John Moores University, UK
Wael El Deredy, Liverpool John Moores University, UK
Paulo G. J. Lisboa, Liverpool John Moores University, UK
Christian L. Dunis, Liverpool John Moores University, UK
Chapter III
Automatically Identifying Predictor Variables for Stock Return Prediction .......................................60
Da Shi, Peking University, China
Shaohua Tan, Peking University, China
Shuzhi Sam Ge, National University of Singapore, Singapore
Chapter IV
Higher Order Neural Network Architectures for Agent-Based
Computational Economics and Finance ...............................................................................................79
John Seiffertt, Missouri University of Science and Technology, USA
Donald C. Wunsch II, Missouri University of Science and Technology, USA
Chapter V
Foreign Exchange Rate Forecasting Using Higher Order Flexible Neural Tree .................................94
Yuehui Chen, University of Jinan, China
Peng Wu, University of Jinan, China
Qiang Wu, University of Jinan, China
Chapter VI
Higher Order Neural Networks for Stock Index Modeling ...............................................................113
Yuehui Chen, University of Jinan, China
Peng Wu, University of Jinan, China
Qiang Wu, University of Jinan, China
Section II
Artificial Higher Order Neural Networks for Time Series Data
Chapter VII
Ultra High Frequency Trigonometric Higher Order Neural Networks
for Time Series Data Analysis ............................................................................................................133
Ming Zhang, Christopher Newport University, USA
Chapter VIII
Artificial Higher Order Pipeline Recurrent Neural Networks
for Financial Time Series Prediction ..................................................................................................164
Panos Liatsis, City University, London, UK
Abir Hussain, John Moores University, UK
Efstathios Milonidis, City University, London, UK
Chapter IX
A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction ....................190
Abir Hussain, John Moores University, UK
Panos Liatsis, City University, London, UK
Chapter X
Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction .......212
David R. Selviah, University College London, UK
Janti Shawash, University College London, UK
Chapter XI
Artificial Higher Order Neural Networks in Time Series Prediction .................................................250
Godfrey C. Onwubolu, University of the South Pacific, Fiji
Chapter XII
Application of Pi-Sigma Neural Networks and Ridge Polynomial Neural Networks
to Financial Time Series Prediction ....................................................................................................271
Rozaida.Ghazali,.Liverpool.John.Moores.University,.UK
. Dhiya.Al-Jumeily,.Liverpool.John.Moores.University,.UK
Section III
Artificial Higher Order Neural Networks for Business
Chapter XIII
Electric Load Demand and Electricity Prices Forecasting Using Higher Order Neural Networks
Trained by Kalman Filtering ...............................................................................................................295
Edgar.N..Sanchez,.CINVESTAV,.Unidad.Guadalajara,.Mexico
. Alma.Y..Alanis,.CINVESTAV,.Unidad.Guadalajara,.Mexico
. Jesús.Rico,.Universidad.Michoacana.de.San.Nicolas.de.Hidalgo,.Mexico
Chapter XIV
Adaptive Higher Order Neural Network Models and Their Applications in Business .......................314
Shuxiang.Xu,.University.of.Tasmania,.Australia
Chapter XV
CEO Tenure and Debt: An Artificial Higher Order Neural Network Approach .................................330
Jean.X..Zhang,.George.Washington.University,.USA
Chapter XVI
Modelling and Trading the Soybean-Oil Crush Spread with Recurrent
and Higher Order Networks: A Comparative Analysis .......................................................................348
Christian.L..Dunis,.CIBEF,.and.Liverpool.John.Moores.University,.UK
. Jason.Laws,.CIBEF,.and.Liverpool.John.Moores.University,.UK
. Ben.Evans,.CIBEF,.and.Dresdner-Kleinwort-Investment.Bank.in.Frankfurt,.Germany
Section IV
Artificial Higher Order Neural Networks Fundamentals
Chapter XVII
Fundamental Theory of Artificial Higher Order Neural Networks .....................................................368
Madan.M..Gupta,.University.of.Saskatchewan,.Canada
. Noriyasu.Homma,.Tohoku.University,.Japan
. Zeng-Guang.Hou,.The.Chinese.Academy.of.Sciences,.China
. Ashu.M..G..Solo,.Maverick.Technologies.America.Inc.,.USA
. Takakuni.Goto,.Tohoku.University,.Japan
Chapter XVIII
Dynamics in Artificial Higher Order Neural Networks with Delays .................................................389
Jinde Cao, Southeast University, China
Fengli Ren, Southeast University, China
Jinling Liang, Southeast University, China
Chapter XIX
A New Topology for Artificial Higher Order Neural Networks: Polynomial Kernel Networks .......430
Zhao Lu, Tuskegee University, USA
Leang-san Shieh, University of Houston, USA
Guanrong Chen, City University of Hong Kong, China
Chapter XX
High Speed Optical Higher Order Neural Networks for Discovering Data Trends
and Patterns in Very Large Databases ................................................................................................442
David R. Selviah, University College London, UK
Chapter XXI
On Complex Artificial Higher Order Neural Networks: Dealing with Stochasticity,
Jumps and Delays ..............................................................................................................................466
Zidong Wang, Brunel University, UK
Yurong Liu, Yangzhou University, China
Xiaohui Liu, Brunel University, UK
Chapter XXII
Trigonometric Polynomial Higher Order Neural Network Group Models
and Weighted Kernel Models for Financial Data Simulation and Prediction ....................................484
Lei Zhang, University of Technology, Sydney, Australia
Simeon J. Simoff, University of Western Sydney, Australia
Jing Chun Zhang, IBM, Australia
About the Contributors ...................................................................................................................504
Index ................................................................................................................................................514
Detailed Table of Contents
Preface ...............................................................................................................................................xvii
Acknowledgment .............................................................................................................................xxiii
Section I
Artificial Higher Order Neural Networks for Economics
Chapter I
Artificial Higher Order Neural Network Nonlinear Models: SAS NLIN or HONNs? ..........................1
Ming Zhang, Christopher Newport University, USA
This chapter delivers general format of Higher Order Neural Networks (HONNs) for nonlinear data
analysis and six different HONN models. This chapter mathematically proves that HONN models could
converge and have mean squared errors close to zero. This chapter illustrates the learning algorithm
with update formulas. HONN models are compared with SAS Nonlinear (NLIN) models and results
show that HONN models are 3 to 12% better than SAS Nonlinear models. Moreover, this chapter shows
how to use HONN models to find the best model, order and coefficients, without writing the regression
expression, declaring parameter names, and supplying initial parameter values.
Chapter II
Higher Order Neural Networks with Bayesian Confidence Measure for the Prediction
of the EUR/USD Exchange Rate .........................................................................................................48
Adam Knowles, Liverpool John Moores University, UK
Abir Hussain, Liverpool John Moores University, UK
Wael El Deredy, Liverpool John Moores University, UK
Paulo G. J. Lisboa, Liverpool John Moores University, UK
Christian L. Dunis, Liverpool John Moores University, UK
Multi-Layer Perceptrons (MLP) are the most common type of neural network in use, and their ability to
perform complex nonlinear mappings and tolerance to noise in data is well documented. However, MLPs
also suffer long training times and often reach only local optima. Another type of network is Higher
Order Neural Networks (HONN). These can be considered a ‘stripped-down’ version of MLPs, where
joint activation terms are used, relieving the network of the task of learning the relationships between
the inputs. The predictive performance of the network is tested with the EUR/USD exchange rate and
evaluated using standard financial criteria including the annualized return on investment, showing a 8%
increase in the return compared with the MLP. The output of the networks that give the highest annual-
ized return in each category was subjected to a Bayesian based confidence measure.
Chapter III
Automatically Identifying Predictor Variables for Stock Return Prediction .......................................60
Da Shi, Peking University, China
Shaohua Tan, Peking University, China
Shuzhi Sam Ge, National University of Singapore, Singapore
Real-world financial systems are often nonlinear, do not follow any regular probability distribution, and
comprise a large amount of financial variables. Not surprisingly, it is hard to know which variables are
relevant to the prediction of the stock return based on data collected from such a system. In this chapter,
we address this problem by developing a technique consisting of a top-down part using an artificial
Higher Order Neural Network (HONN) model and a bottom-up part based on a Bayesian Network (BN)
model to automatically identify predictor variables for the stock return prediction from a large financial
variable set. Our study provides an operational guidance for using HONN and BN in selecting predictor
variables from a large amount of financial variables to support the prediction of the stock return, includ-
ing the prediction of future stock return value and future stock return movement trends.
Chapter IV
Higher Order Neural Network Architectures for Agent-Based
Computational Economics and Finance ...............................................................................................79
John Seiffertt, Missouri University of Science and Technology, USA
Donald C. Wunsch II, Missouri University of Science and Technology, USA
As the study of agent-based computational economics and finance grows, so does the need for appropri-
ate techniques for the modeling of complex dynamic systems and the intelligence of the constructive
agent. These methods are important where the classic equilibrium analytics fail to provide sufficiently
satisfactory understanding. In particular, one area of computational intelligence, Approximate Dynamic
Programming, holds much promise for applications in this field and demonstrate the capacity for artificial
Higher Order Neural Networks to add value in the social sciences and business. This chapter provides
an overview of this area, introduces the relevant agent-based computational modeling systems, and sug-
gests practical methods for their incorporation into the current research. A novel application of HONN
to ADP specifically for the purpose of studying agent-based financial systems is presented.
Chapter V
Foreign Exchange Rate Forecasting Using Higher Order Flexible Neural Tree .................................94
Yuehui Chen, University of Jinan, China
Peng Wu, University of Jinan, China
Qiang Wu, University of Jinan, China