Neural Networks for Financial Forecasting / Edition 1

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Overview

Neural networks are interconnecting systems set up to function like the neuron patterns of the human brain, learning by a process of trial and error. When applied to the world of finance, neural networks are automated trading systems, based on mapping inputs and outputs for forecasting probable future values.

In Neural Networks for Financial Forecasting--the first book to focus on the role of neural networks specifically in price forecasting--traders are provided with a solid foundation that explains how neural nets work, what they can accomplish, and how to construct, use, and apply them for maximum profit. It is written by an acknowledged authority who is, himself, the developer of several successful networks.

Beginning with an examination of the structure of a typical network, the author defines what they can and cannot predict. Then, step-by-step, he explains how to design, build, train, and use exactly the kind of network that best suits your forecasting needs, from deciding what is to be predicted and selecting the appropriate inputs, to designing the network architecture and training algorithms to meet your specific goals. Guidelines help you determine when to stop training, and there are tips on what to try if your network won't train, or memorizes rather than generalizes. Also included are discussions on the amount of data you'll need, as well as the preprocessing of data, so that it is in a form usable to the network.

Most importantly, you'll learn how to bring all the elements together. Neural Networks for Financial Forecasting enables you to develop a usable, state-of-the-art network from scratch all the way through completion of training. There are spreadsheets and graphs throughout to illustrate key points, and an appendix of valuable information, including neural network software suppliers and related publications. This is comprehensive, targeted information on some of the most important technology in finance today.

A step-by-step guide for designing, building, and training the cutting-edge technology of neural networks!

Here is everything you'll need to implement a neural network designed to meet your specific forecasting needs. Written by an acknowledged authority, who is the developer of several successful networks himself. You'll learn:
* The structure of a typical network
* What can and cannot be predicted
* How to select proper inputs
* The best network architecture and training algorithms
* How to structure and pretest data
* What to do if the network does not train.

With spreadsheets and graphs illustrating key points, Neural Networks for Financial Forecasting also includes an appendix of neural net software suppliers, as well as a listing of useful publications: a complete foundation for all financial professionals.

The Wiley Trader's Advantage is a series of concise, highly focused books designed to keep savvy futures, options, stocks, bonds, and commodities traders abreast of the latest successful strategies and techniques used by the keenest minds in the business. Each title delivers timely, cutting-edge guidance on a key aspect of trading, including trading systems, portfolio management methods, computerized forecasting, and systems optimization.

It sounds too good to be true. A small group of savvy traders are tapping into the intuitive, number-crunching power of neural networks to pinpoint trending markets and make more profitable decisions. Now Ed Gately shows the rest of us how to use, construct, and apply neural nets--the brains of artificial intelligence--to automate trading systems for maximum profit.

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Product Details

  • ISBN-13: 9780471112129
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 10/28/1995
  • Series: Wiley Trader's Exchange Series , #6
  • Edition number: 1
  • Pages: 169
  • Product dimensions: 6.32 (w) x 9.38 (h) x 0.70 (d)

Meet the Author

EDWARD GATELY, an authority on computerized trading, designs and tests trading systems, and develops neural networks for financial professionals and traders. He is a frequent lecturer for CompuTrac on the topic of neural nets. He lives in Mahwah, New Jersey.

About the Series Editor : PERRY J. KAUFMAN has been designing and developing trading strategies and hedge and arbitrage programs in derivatives markets since 1970. He is the author of six books, including The New Commodity Trading Systems and Methods and Smarter Trading. Mr. Kaufman is a principal at Kaufman Diamond & Yeong, an international consulting firm with offices in Vermont, Chicago, and Singapore.

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Table of Contents

Biological Brain and Artificial Neural Networks (ANN).

Overview of Artificial Neural Networks.

Choosing What Is to Be Predicted and Selecting Appropriate Inputs.

Input Selection.

Preparing Data.

Other Ways to Manipulate Data.

Training and Network Architectures.

Lessons in Starting and Stopping Training.

Developing a Network to Forecast, the S&P 500 Ten Days into the Future.

Appendices.

Glossary.

Bibliography.

Index.

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  • Anonymous

    Posted March 11, 2000

    a basic overview of neural nets

    Disappointing. Little is said about the theory of neural networks, and the example of a network to forecast the S&P 500 violates one of the basic rules presented earlier in the book: that nets are better at forecasting changes than absolute levels. Basic computer procedures are included, which is superficial to anyone interested in this topic.

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