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This volume looks at financial prediction from a broad range of perspectives. It covers: - the economic arguments - the practicalities of the markets - how predictions are used - how predictions are made - how predictions are turned into something usable (asset locations) It combines a discussion of standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All 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. Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.
- Sales Rank: #1221367 in Books
- Published on: 2013-10-04
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .65" w x 6.10" l, .90 pounds
- Binding: Paperback
- 273 pages
About the Author
John G. Taylor is Emeritus Professor and Director, Centre for Neural Networks, Kings College, University of London, and Guest Scientist, Institute of Medicine, Research Centre, Juelich, Germany. He has published more than four hundred papers in quantum field theory, elementary particle physics, string theory, neural networks, time series, pattern recognition, and cognitive neuroscience. He is also a prominent debunker of pseudoscience.
Most helpful customer reviews
30 of 31 people found the following review helpful.
Misleading and Unorganized
By Matteo Gadioli
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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