Neural Networks in Finance

Gaining Predictive Edge in the Market

Paul McNelis - Collection Advanced Finance Series

250 pages, parution le 31/01/2005

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Résumé

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.

Audience

Upper division undergraduates and MBA students, as well as the rapidly growing number of financial engineering programs, whose curricula emphasize quantitative applications in financial economics and markets

L'auteur - Paul McNelis

Paul McNelis ,Professor of International Economic and Financial Policy at Fordham University Graduate School of Business. Professor of Economics at Georgetown University until 2004.

Sommaire

Introduction
Econometric Foundations
- What Are Neural Networks
- Estimation of a Network with Evolutionary Computation
- Evaluation of Network Estimation
Application and Examples
- Estimation and Forecasting with Artificial Data
- Times Series: Examples from Industry and Finance
- Inflation and Deflation: Hong Kong and Japan
- Classification: Credit Card Default and Bank Failures
- Dimensionality Reduction and Implied Volatility Forecasting

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Academic Press
Auteur(s)	Paul McNelis
Collection	Advanced Finance Series
Parution	31/01/2005
Nb. de pages	250
Format	16 x 23,5
Couverture	Relié
Poids	540g
Intérieur	Noir et Blanc
EAN13	9780124859678
ISBN13	978-0-12-485967-8