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Bibliographic Details

Title: Neural Networks for Financial Forecasting (Wiley Trader's Exchange)
Publisher: Wiley
Publication Date: 1995
Language: English
ISBN 10: 0471112127
ISBN 13: 9780471112129
Binding: Hardcover
Edition: 1st Edition
Condition: Very Good
Dust Jacket Condition: No Jacket

About this title

Synopsis

A small number of stock market traders are now using neural networks to pinpoint profitable markets. This guide shows investors how to apply neural networks, the brains of artificial intelligence, to the automation of trading systems for maximum profit. Readers will discover how to master the organizing principles of a typical neural network, choose which market moves are to be predicted, and select proper inputs. The volume features an appendix of neural network suppliers.

From the Back Cover

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.

"About this title" may belong to another edition of this title.

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