Hybrid-Learning Methods for Stock Index Modeling

Hybrid-Learning Methods for Stock Index Modeling

Yuehui Chen (Jinan University, P. R. China) and Ajith Abraham (Chung-Ang University, Republic of Korea)
Copyright: © 2006 |Pages: 16
DOI: 10.4018/978-1-59140-670-9.ch004
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The use of intelligent systems for stock market prediction has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we consider the Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index. We analyze 7-year Nasdaq 100 main-index values and 4-year NIFTY index values. This chapter investigates the development of novel, reliable, and efficient techniques to model the seemingly chaotic behavior of stock markets. We consider the flexible neural tree algorithm, a wavelet neural network, local linear wavelet neural network, and finally a feed-forward artificial neural network. The particle-swarm-optimization algorithm optimizes the parameters of the different techniques. This paper briefly explains how the different learning paradigms could be formulated using various methods and then investigates whether they can provide the required level of performance — in other words, whether they are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the models considered could represent the stock indices behavior very accurately.

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Table of Contents
Joarder Kamruzzaman, Rezaul Begg, Ruhul Sarker
Joarder Kamruzzaman, Rezaul Begg, Ruhul Sarker
Chapter 1
Joarder Kamruzzaman, Ruhul A. Sarker
The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major... Sample PDF
Artificial Neural Networks: Applications in Finance and Manufacturing
Chapter 2
Ruhul A. Sarker, Hussein A. Abbass
Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modeling complex real-world problems. One of the... Sample PDF
Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks
Chapter 3
David Encke
Researchers have known for some time that nonlinearity exists in the financial markets and that neural networks can be used to forecast market... Sample PDF
Neural Network-Based Stock Market Return Forecasting Using Data Mining for Variable Reduction
Chapter 4
Yuehui Chen, Ajith Abraham
The use of intelligent systems for stock market prediction has been widely established. In this paper, we investigate how the seemingly chaotic... Sample PDF
Hybrid-Learning Methods for Stock Index Modeling
Chapter 5
John Fulcher, Ming Zhang, Shuxiang Xu
Financial time-series data is characterized by nonlinearities, discontinuities, and high-frequency multipolynomial components. Not surprisingly... Sample PDF
Application of Higher-Order Neural Networks to Financial Time-Series Prediction
Chapter 6
Masoud Mohammadian, Mark Kingham
In this chapter, an intelligent hierarchical neural network system for prediction and modelling of interest rates in Australia is developed. A... Sample PDF
Hierarchical Neural Networks for Modelling Adaptive Financial Systems
Chapter 7
Sumit Kumar Bose, Janardhanan Sethuraman, Sadhalaxmi Raipet
The term structure of interest rates holds a place of prominence in the financial and economic world. Though there is a vast array of literature on... Sample PDF
Forecasting the Term Structure of Interest Rates Using Neural Networks
Chapter 8
Joarder Kamruzzaman, Ruhul A. Sarker, Rezaul K. Begg
In today’s global market economy, currency exchange rates play a vital role in national economy of the trading nations. In this chapter, we present... Sample PDF
Modeling and Prediction of Foreign Currency Exchange Markets
Chapter 9
Tong-Seng Quah
Artificial neural networks’ (ANNs’) generalization powers have in recent years received admiration of finance researchers and practitioners. Their... Sample PDF
Improving Returns on Stock Investment through Neural Network Selection
Chapter 10
Eldon Gunn, Corinne MacDonald
This chapter provides some examples from the literature of how feed-forward neural networks are used in three different contexts in manufacturing... Sample PDF
Neural Networks in Manufacturing Operations
Chapter 11
M. Imad Khan, Saeid Nahavandi, Yakov Frayman
This chapter presents the application of a neural network to the industrial process modeling of high-pressure die casting (HPDC). The large number... Sample PDF
High-Pressure Die-Casting Process Modelling Using Neural Networks
Chapter 12
Sergio Cavalieri, Paolo Maccarrone, Roberto Pinto
The estimation of the production cost per unit of a product during its design phase can be extremely difficult, especially if information about... Sample PDF
Neural Network Models for the Estimation of Product Costs: An Application in the Automotive Industry
Chapter 13
Tapabrata Ray
Surrogate-assisted optimization frameworks are of great use in solving practical computationally expensive process-design-optimization problems. In... Sample PDF
A Neural-Network-Assisted Optimization Framework and Its Use for Optimum-Parameter Identification
Chapter 14
George A. Rovithakis, Stelios E. Perrakis, Manolis A. Christodoulou
In this chapter, a neuroadaptive scheduling methodology, approaching machine scheduling as a control-regulation problem, is presented and evaluated... Sample PDF
Artificial Neural Networks in Manufacturing: Scheduling
Chapter 15
Bernard F. Rolfe, Yakov Frayman, Georgina L. Kelly, Saeid Nahavandi
This chapter describes the application of neural networks to recognition of lubrication defects typical to industrial cold forging process. The... Sample PDF
Recognition of Lubrication Defects in Cold Forging Process with a Neural Network
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