"This book clarifies many of the mysteries of Neural Networks and related optimization techniques for researchers in both economics and finance. It contains many practical examples backed up with computer programs for readers to explore. I recommend it to anyone who wants to understand methods used in nonlinear forecasting."
-- Blake LeBaron, Professor of Finance, Brandeis University
"An important addition to the select collection of books on financial econometrics, Paul Mcnelis' volume, Neural Networks in Finance, serves as an important reference on neural network models of nonlinear dynamics as a practical econometric tool for better decision-making in financial markets."
-- Roberto S. Mariano, Dean of School of Economics and Social Sciences & Vice-Provost for Research, Singapore Management University; Professor Emeritus of Economics, University of Pennsylvania
"This book represents an impressive step forward in the exposition and application of evolutionary computational tools. The author illustrates the potency of evolutionary computational tools through multiple examples, which contrast the predictive outcomes from the evolutionary approach with others of a linear and general non-linear variety. The book will be of utmost appeal to both academics throughout the social sciences as well as practitioners, especially in the area of finance."
-- Carlos Asilis, Portfolio Manager, VegaPlus Capital Partners; formerly Chief Investment Strategist, JPMorgan Chase
"an excellent, easy-to read introduction to the math behind neural networks."
- Financial Engineering News
Customer Reviews & Comments
Defiantly more of a math book than a programming guide, but that was what I was expecting. This book explains how to use neural networks in the field of finance. It does so very logically and mathematically. You are shown how to apply neural networks to many different financial problems. But you are mostly left to yourself to actually implement the neural networks on a computer system. Some example source code is provided for MathCad, which is an expensive software package you can buy separately.
If you are already comfortable with neural network programming, and are looking to learn to apply neural networks to finance, this book is great. Being a Java programmer I used the open source JOONE package to implement some of the book's examples in Java. Though JOONE is not suited to all examples in the book, it is a good start for a Java programmer.
The book shows how neural networks can be applied to many real world financial problems. The book pays particular interest to international finance. The book examines Hong Kong and Japan, examining inflation, deflation, currency volatility, and other issues.
I found the book to be very useful in giving me an introduction to neural networks in finance.
The table of contents follows:
Chapter 1: Introduction
Part 1: Econometric Foundations
Chapter 2: What Are Neural Networks?
Chapter 3: Estimation of a Network with Evolutionary Computation
Chapter 4: Evaluation of Network Estimation
Part 2: Applications and Examples
Chapter 5: Estimating and Forecasting with Artificial Data
Chapter 6: Time Series: Examples from Industry and Finance
Chapter 7: Inflation and Deflation: Hong Kong and Japan
Chapter 8: Classification: Credit Card Default and Bank Failures
Chapter 9: Dimensionality Reduction and Implied Volatility Forecasting
