Forecasting Stock Returns with Artificial Neural Networks

Forecasting Stock Returns with Artificial Neural Networks

Suraphan Thawornwong (Thailand Securities Depository, Thailand) and David Enke (University of Missouri - Rolla, USA)
Copyright: © 2004 |Pages: 33
DOI: 10.4018/978-1-59140-176-6.ch003
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Abstract

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

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Table of Contents
Preface
G. Peter Zhang
Chapter 1
G. Peter Zhang
Artificial neural networks have emerged as an important quantitative modeling tool for business forecasting. This chapter provides an overview of... Sample PDF
Business Forecasting with Artificial Neural Networks: An Overview
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Chapter 2
Leonard J. Parsons, Ashutosh Dixit
Marketing managers must quantify the effects of marketing actions on contemporaneous and future sales performance. This chapter examines forecasting... Sample PDF
Using Artificial Neural Networks to Forecast Market Response
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Chapter 3
Suraphan Thawornwong, David Enke
During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In... Sample PDF
Forecasting Stock Returns with Artificial Neural Networks
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Chapter 4
Steven Walczah
Forecasting financial time series with neural networks is problematic. Multiple decisions, each of which affects the performance of the neural... Sample PDF
Forecasting Emerging Market Indexes with Neural Networks
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Chapter 5
David West, Scott Dellana
The quality of treated wastewater has always been an important issue, but it becomes even more critical as human populations increase.... Sample PDF
Predicting WastewaterBOD Levels with Neural Network Time Series Models
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Chapter 6
Rob Law, Ray Pine
Practical tourism forecasters concentrate primarily on quantitative causal-relationship and time series methods. Although these traditional... Sample PDF
Tourism Demand Forecasting for the Tourism Industry: A Neural Network Approach
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Chapter 7
Melody Y. Kiang, Dorothy M. Fisher, Michael Y. Hu, Robert T. Chi
This chapter presents an extended Self-Organizing Map (SOM) network and demonstrates how it can be used to forecast market segment membership. The... Sample PDF
Using an Extended Self-Organizing Map Network to Forecast Market Segment Membership
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Chapter 8
Kidong Lee, David Booth, Pervaiz Alam
The back-propagation (BP) network and the Kohonen self-organizing feature map, selected as the representative types for the supervised and... Sample PDF
Backpropagation and Kohonen Self-Organizing Feature Map in Bankruptcy Prediction
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Chapter 9
Michael Y. Hu, Murali Shanker, Ming S. Hung
This study shows how neural networks can be used to model posterior probabilities of consumer choice and a backward elimination procedure can be... Sample PDF
Predicting Consumer Situational Choice with Neural Networks
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Chapter 10
Leong-Kwan Li, Wan-Kai Pang, Wing-Tong Yu, Marvin D. Troutt
Movements in foreign exchange rates are the results of collective human decisions, which are the results of the dynamics of their neurons. In this... Sample PDF
Forecasting Short-Term Exchange Rates: A Recurrent Neural Network Approach
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Chapter 11
G. Peter Zhang
This chapter presents a combined ARIMA and neural network approach for time series forecasting. The model contains three steps: (1) fitting a linear... Sample PDF
A Combined ARIMA and Neural Network Approach for Time Series Forecasting
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Chapter 12
Douglas M. Kline
In this study, we examine two methods for Multi-Step forecasting with neural networks: the Joint Method and the Independent Method. A subset of the... Sample PDF
Methods for Multi-Step Time Series Forecasting Neural Networks
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Chapter 13
Bradley H. Morantz, Thomas Whalen, G. Peter Zhang
In this chapter, we propose a neural network based weighted window approach to time series forecasting. We compare the weighted window approach with... Sample PDF
A Weighted Window Approach to Neural Network Time Series Forecasting
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Chapter 14
Satish Nargundkar, Jennifer Lewis Priestley
In this chapter, we examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic... Sample PDF
Assessment of Evaluation Methods for Prediction and Classifications of Consumer Risk in the Credit Industry
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About the Authors