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

Hardcover (Print)
Buy New
Buy New from BN.com
$84.99
Used and New from Other Sellers
Used and New from Other Sellers
from $39.75
Usually ships in 1-2 business days
(Save 63%)
Other sellers (Hardcover)
  • All (7) from $39.75   
  • New (3) from $71.69   
  • Used (4) from $39.75   

Overview

[back jacket]

Business/Finance

Neural Networks in Finance
Gaining Predictive Edge in the Market

Paul McNelis

"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… 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

Neural Networks in Finance 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. The text shows that these networks are easy to implement and interpret once the time-honored quest for closed form solutions is reconsidered.

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. Numerical illustrations use MATLAB code and the book is accompanied by a website.

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

Read More Show Less

Editorial Reviews

From the Publisher
"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

Read More Show Less

Product Details

  • ISBN-13: 9780124859678
  • Publisher: Elsevier Science
  • Publication date: 1/5/2005
  • Series: Academic Press Advanced Finance Series
  • Edition number: 1
  • 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
Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)