Features
- Cover Type: Paperback with 273 pages
- Published by: Springer
- Edition: 1st Edition September 17, 2002
- Written in: English
- ISBN 10 Number: 1852335319
- ISBN 13 Number: 978-1852335311
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Book Dimensions:
9.2 x 6.1 x 0.7 inches
- Weighs: 15.2 ounces
Product Description
Looks at financial prediction from a broad range of perspectives. All of the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets. Softcover.
Reader Reviews
This is the typical book created by putting together technical papers, proceedings, and working papers without a unifying structure. This is a short list of this book's limitations: 1) Fragmented: every chapter is written by a different author. 2) Unorganized: Neural Networks are introduced only at chapter 11. 3) So badly planned that both chapter 11 and 18 have basically the same content. You can look inside the book yourself to see that. 4) Lack of examples: very few implementations of NN are provided or suggested. 5) Out of context: many chapters are not related to Neural Networks at all, for example chapter 16 is about Yield curve modelling, and chapter 21 is dedicated to Portfolio Optimization without any contextual reference to NN. Please be aware that after introducing these topics there is NO follow-up whatsoever with NN application examples. 6) Misleading: The content about Neural Networks is really minimal.
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