Neural Networks in Finance: Gaining Predictive Edge in the Market / Edition 1

Neural Networks in Finance: Gaining Predictive Edge in the Market / Edition 1

by Paul D. McNelis
     
 

ISBN-10: 0124859674

ISBN-13: 9780124859678

Pub. Date: 01/05/2005

Publisher: Elsevier Science

This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction.

McNelis utilizes a variety of examples, from forecasting

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Overview

This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction.

McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong.

• Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance
• Includes numerous examples and applications
• Numerical illustrations use MATLAB code and the book is accompanied by a website

Product Details

ISBN-13:
9780124859678
Publisher:
Elsevier Science
Publication date:
01/05/2005
Series:
Academic Press Advanced Finance Series
Pages:
256
Product dimensions:
6.20(w) x 9.24(h) x 0.80(d)

Table of Contents

Preface; 1. Introduction; 2. What Are Neural Networks; 3. Estimation of a Network with Evolutionary Computation; 4. Evaluation of Network Estimation; 5. Estimation and Forecasting with Artificial Data; 6. Times Series: Examples from Industry and Finance; 7. Inflation and Deflation: Hong Kong and Japan; 8. Classification: Credit Card Default and Bank Failures; 9. Dimensionality Reduction and Implied Volatility Forecasting

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