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Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance) Hardcover – January 5, 2005


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Product Details

  • Series: Academic Press Advanced Finance
  • Hardcover: 256 pages
  • Publisher: Academic Press; 1 edition (January 5, 2005)
  • Language: English
  • ISBN-10: 0124859674
  • ISBN-13: 978-0124859678
  • Product Dimensions: 6 x 0.6 x 9 inches
  • Shipping Weight: 7.2 ounces (View shipping rates and policies)
  • Average Customer Review: 3.8 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #754,775 in Books (See Top 100 in Books)

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

Customer Reviews

3.8 out of 5 stars
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Most Helpful Customer Reviews

33 of 35 people found the following review helpful By Jeffrey Heaton VINE VOICE on June 12, 2006
Format: Hardcover Verified Purchase
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
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20 of 21 people found the following review helpful By Sam on March 23, 2008
Format: 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.
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1 of 1 people found the following review helpful By Robin T. Wernick on February 21, 2013
Format: Hardcover Verified Purchase
"Neural Networks in Finance" whets the appetite for knowledge on how to build a predictive finanial index. It provides a generally good, but shallow, introduction to the construction of neural nets and gives a few examples implemented in MATLAB.

Unfortunately, it fails to explain how the examples reflect the actions of the networks. Too few examples are given to get any general idea of how to design your own models and only a narrow range of neural models are provided. How the author chose his coefficients is a mystery as well; the discussion is mostly at the highest level before it jumps precipitously into concrete values descending from the ether. Obviously, the mass of financial data preempts the reader from applying MATLAB as an operational tool, it exists as a plotting convenience for the author. The reader is left to his own resources to choose a more suitable(faster) computer language to do the computing and find a way to feed the network the enormous amount of automated data required to keep up with daily changes.

The author does something good to restore his credibility in the last two chapters by tackling the problem of discovering the precursor indications that signal specific changes in credit card default and volitility. The author reveals his firm attachment to statistical methodoligy. There is no apparent attempt to discover the ability of the neural networks to produce an 'intuitive' cognitive prediction of anything. You might find a clue in "Practical Neural Network Recipies in C++".

On the whole, the book is worthwhile, but serves more to produce a background methodology to confirm your suspicions by using an independent method. It has a place in my library and I'm glad I have it, but it doesn't set my mind on fire.
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Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance)
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