Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance) [Hardcover]

Paul D. McNelis (Author)

Book Description

Series: Academic Press Advanced Finance | Publication Date: January 5, 2005

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

Editorial Reviews

Review

"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

Book Description

Provides a thorough and applied view of neural networks and the genetic algorithm in finance

See all Editorial Reviews

Product Details

• Hardcover: 256 pages
• Publisher: Academic Press; 1 edition (January 5, 2005)
• Language: English
• ISBN-10: 0124859674
• ISBN-13: 978-0124859678
• Product Dimensions: 9.4 x 6.3 x 0.8 inches
• Shipping Weight: 7.2 ounces (View shipping rates and policies)
• Average Customer Review: 3.7 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #633,959 in Books (See Top 100 in Books)
  • #85 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Neural Networks

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Customer Reviews

Most Helpful Customer Reviews

31 of 33 people found the following review helpful:

4.0 out of 5 stars More Mathematical than Technical, June 12, 2006

By 

Jeffrey Heaton "Jeff Heaton" (St. Louis, MO) - See all my reviews
(VINE VOICE)   

This review is from: Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance) (Hardcover)

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

17 of 18 people found the following review helpful:

3.0 out of 5 stars Great Book but Horrible Matlab Code, March 23, 2008

By 

Sam (US) - See all my reviews

This review is from: Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance) (Hardcover)

I've only been through the first 4 chapters so far. I found the way the material was presented to be very good and the authors did a very good job presenting and explaining the mater.
Having understood the material which I would credit to the author's great clarity and presentation, I decided to run the Matlab code the author provides. This is were everything started going wrong. The functions are full of error and would not run. I had to make changes to the m-file for the proram to run. This was also very hard since the code is very poorly documented (input variables are not even explained). Even after fixing the erros, the programs did not give the results the author claims. In the example on page 78, the author claims that the genetic algorithm gives a result very close to 4 which is not true (some results were less than 2). I then tried to work the example on page 81. Again I got errors trying to run the program. In the file ffnet9.m, the author has an if statement if the number of arguments is 8 instead of the 12 expected by the function while in the example, the number of arguments is 9 and therefore you get an error trying to run the function ffnet9. second, it seems the author had modified a previous function which took 8 arguments since the function is actually called ffnet8 in the file while the file is called ffnet9.m (very bad programming). After fixing the problem, the linear model gave an R-squared in the 0.55 range and the second degree polynomials gave a result in the range of 0.91 however, the neural network R-squared was in the range of 0.73 and not 0.99 as claimed by the author! the line search in the function fminunc is exiting due to the line search. By the way, don't run the program on page 81 1000 times as done in the for loop as this will take forever and I'm not sure way the author did it.

2 of 4 people found the following review helpful:

4.0 out of 5 stars Great Overview, August 6, 2011

By 

Joshua Wilder - See all my reviews
(REAL NAME)   

This review is from: Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance) (Hardcover)

I was compelled to write this because of the previous comments complaining about poor code and all math. If you are looking for a book that provides you with code to successfully predict the movements of the financial markets then please share it with the rest of us. As for the all math part, I am perfectly capable of writing code based on mathematical formulas. If you aren't comfortable with it then you should invest in a programming language book. I don't need a book full of code (although I have in the past, but because I was not proficient in a language). Given the ample R packages out there, one should easily be able to take these formulas and provide a great jumping off point into neural networks and forecasting. If you are looking for a book that promises big profits and low risk they are usually found on large blinking ads on cheesy finance sites.