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Synopsis
Neural Network Time Series Forecasting of Financial Markets E. Michael Azoff The first comprehensive and practical introduction to using neural networks in financial time series forecasting. This practical working guide shows you how to understand, design and profitably use neural network techniques in financial market forecasting. It encompasses:
- A tutorial introduction to neural networks
- Data preprocessing
- Key network design issues
- Random walk probability theory
- Fully specified benchmarks (and code for implementing the benchmarks as pre–trained networks)
- An overview of futures trading
- Discussion of trading systems and risk management
"synopsis" may belong to another edition of this title.
Synopsis
A practical introduction to the use of neural networks in financial time series forecasting, this monograph encompasses such topics as data preprocessing, random walk probability theory and specified benchmarks (and the code that is used to implement benchmarks as pre-trained networks). There is also an overview of futures trading and discussion of trading systems and risk management. Emphasis is placed on multilater perception networks, which are used in many commercial applications.
About the Author
About the author E. MICHAEL AZOFF is founder and director of Themisto Numerics Ltd, UK, a consultancy specialising in neural network applications, and where he is currently investigating financial market forecasting techniques. Dr Azoff is a chartered engineer, who has published research papers and is also on the editorial board of the learned journal Neural Computing and Applications.
"About this title" may belong to another edition of this title.
- PublisherJohn Wiley & Sons
- Publication date1994
- ISBN 10 0471943568
- ISBN 13 9780471943563
- BindingHardcover
- LanguageEnglish
- Number of pages212
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