Book Description
The book focuses on forecasting foreign exchange rates via artificial neural networks. It creates and applies the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange-rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges. Foreign Exchange Rate Forecasting with Artificial Neural Networks is targeted at both the academic and practitioner audiences. Managers, analysts and technical practitioners in financial institutions across the world will have considerable interest in the book, and scholars and graduate students studying financial markets and business forecast will also have considerable interest in the book.
The book discusses the most important advances in foreign-exchange-rate forecasting and then systematically develops a number of new, innovative, and creatively crafted neural network models that reduce the volatility and speculative risk in the forecasting of foreign exchange rates. The book discusses and illustrates three general types of ANN models. Each of these model types reflect the following innovative and effective characteristics: (1) The first model type is a three-layer, feed-forward neural network with instantaneous learning rates and adaptive momentum factors that produce learning algorithms (both online and offline algorithms) to predict foreign exchange rates. (2) The second model type is the three innovative hybrid learning algorithms that have been created by combining ANNs with exponential smoothing, generalized linear auto-regression, and genetic algorithms. Each of these three hybrid algorithms has been crafted to forecast various aspects synergetic performance. (3) The third model type is the three innovative ensemble learning algorithms that combining multiple neural networks into an ensemble output. Empirical results reveal that these creative models can produce better performance with high accuracy or high efficiency.
Customer Reviews:
Guide to literature, especially authors' own works.......2007-08-24
I don't know why anyone with a great NN or other trading system would write about it, though that's what I hoped would be in this book. The Good: Lots of discussion about architectures (especially hybrid systems), training methods, and literature references. The Bad (IMHO): I felt deprived by the scant experience and method details and the marginal editing. There is a fair amount of vector algebra for devotees. Overall, despite disappointments, it's probably worth the price for a serious developer or academic researcher.
As a quality general textbook, I recommend 'Neural Networks: A Comprehensive Foundation' 3rd Ed. by Simon Haykin.
Average customer rating:
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Neural Network Time Series: Forecasting of Financial Markets
E. Michael Azoff
Manufacturer: John Wiley & Sons
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ASIN: 0471943568 |
Customer Reviews:
Better than I expected.......1997-02-26
A good introduction to neural nets and their applicability tothe futures markets. Provides mathematical/theoretical basis fortechniques involved in neural net training, testing, chaos and touches on nonlinear systems in general. Also provides interesting benchmarks of several nets against several time series. Great bibliography. Includes Fortran source code for running trained nets but not for training a net.
Average customer rating:
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Neural Networks in Business Forecasting
Manufacturer: Information Science Publishing
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Neural Networks in Business: Techniques and Applications
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Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
ASIN: 1591402158 |
Book Description
Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. Neural Networks in Business Forecasting provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.
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Intelligent and Other Computational Techniques in Insurance: Theory and Applications (Series on Innovative Intelligence, 6)
Manufacturer: World Scientific Publishing Company
ProductGroup: Book
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ASIN: 9812387188 |
Book Description
This book presents recent advances in the theory and implementation of intelligent and other computational techniques in the insurance industry. The paradigms covered encompass artificial neural networks and fuzzy systems, including clustering versions, optimization and resampling methods, algebraic and Bayesian models, decision trees and regression splines. Thus, the focus is not just on intelligent techniques, although these constitute a major component; the book also deals with other current computational paradigms that are likely to impact on the industry. The application areas include asset allocation, asset and liability management, cash-flow analysis, claim costs, classification, fraud detection, insolvency, investments, loss distributions, marketing, pricing and premiums, rate-making, retention, survival analysis, and underwriting.
Average customer rating:
- A good book for researchers.
- Good exposition of AI and a financial application
|
Intelligent Systems and Financial Forecasting
Jason Kingdon
Manufacturer: Springer-Verlag Telos
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ASIN: 3540760989 |
Customer Reviews:
A good book for researchers........1998-05-04
This is a good book, but potential buyers should realize that it is aimed at researchers, not traders. It would take considerable work to rediscover the successful neural net discussed in the book, and there is no guarantee that the net would do well in real trading because the author does not seem to have taken slippage and commissions into account.
Good exposition of AI and a financial application.......1997-10-04
This book contains a number of new and interesting insights into the application of neural networks and genetic algorithms for finance. The idea of using "network regression pruning" looks extremely powerful and it would be very interesting to see this compared with other network pruning techniques. An aspect also worth highlighting is the detailed treatment of the financial experiments which appear to have been conducted in a rigorous and careful manner. I have attended many seminars and talks in which the experimentation for financial applications has been extremely poor. In many instances there is a failure to establish objective test criteria. In this work a single test run on ten years' worth of data is conducted and the algorithm performs well, contradicting all forms of the efficient market hypothesis. The level of detail also allows the reader to reconstruct the algorithm and methods for their own use. Other books on this subject should be as clear and as open.
Average customer rating:
- Worth the money
- Gately's results are very misleading
- poor and misleading
- This book says very little about Neural Networks
- Succinct; clear and moderately comprehensive overview.
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Neural Networks for Financial Forecasting (Wiley Trading)
Edward Gately
Manufacturer: John Wiley & Sons
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The Predictors
ASIN: 0471112127 |
Book Description
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.
Customer Reviews:
Worth the money.......2003-06-19
What does this book promise?
"Succinctly explains how neural networks function, what they can accomplish as well as how to use, construct and apply them for maximum profit."
Ok, so perhaps it should say .... An introductory text with basic definitions and a example of how to use a neural network to predict the S & P 500. But even on the grander promise this book delivers. The book is too basic for those previously familiar with NNs, but perfect for someone who merely would like to begin and fears the prospect of becoming too frustrated to finish. Gately points out potential snares and discusses how to overcome them and walks the reader through a project. He describes how to preprocess the data to ensure meaningful results.
I have followed his steps and have been very please with my project, this book and the value I received when purchasing it.
Gately's results are very misleading.......1999-12-18
The reviewer that wrote the poor and misleading review described the book perfectly.
In a 4 page passage of the book I found at least 10 errors involving contradictions between text, tables and figures. This becomes very frustrating if you are trying to reproduce the results Gately achieved--a task I have found impossible.
Gately also claims great success however he does not test his neural nets on a validation set of data. This is a fatal error in real trading. It is therefor impossible to determine if he was successful at all.
It is clear that Gately does not have a background in quantitative sciences, as he has little skill in accurately and completely reporting procedures and results.
The idea of the book and the trial and error approach is good but another author should write the second edition.
poor and misleading.......1999-09-28
About the only positive thing I can say is that he has done a good job at capturing the feel of the trial and error process that is so necessary in developing a neural network model. That said, I could write a book about what's wrong with the book. There is so much poor advice and misinformation in the book, I would scarcely know where to begin. As I read it, I literally cringed at the so-called "information" being passed on to the unaware.
His errors includes poor sampling of test data, no validation data, poor data preparation, highly correlated inputs, small sample size, basing design changes in on insignificant changes in error, etc.
The extensive bibliography may be useful to some, but I would bet my net worth that it is not a list of books he has read. If you have a copy of Azoff's book on forecasting, you will find that Gately essentially cribbed the bulk of his bibliography from Azoff. The few remaining entries were cribbed from the bibliography found in the Neuroshell literature. The book is fairly heavy with hype for the Neuroshell product. (Not that it's a bad product.)
If you're thinking of buying this, I would recommend instead Master's Practical Neural Networks in C++. Doesn't matter if you don't program, it has plenty of good text, and most importantly, sound advice.
This book says very little about Neural Networks.......1998-05-09
This book offers a lot and delivers very little. For one thing, it gives no theory of NN, instead it wastes one full chapter on the brain and NN's. The front page offers to show "Top techniques for designing and applying the latest.....", but this is false advertizing!. In short, VERY DISAPPOINTING!.
Succinct; clear and moderately comprehensive overview........1998-02-22
Great for intermediate level investors who want to explore neural networks as they apply to financial markets. Author sticks to the overview process. One case study is used to illustrate procedures and concepts. Work is reasonably paced. I recommend it as a good starting point for neural network analysis. Requires some background in financial markets and Excel. Could benefit from including a CD of examples and concept elaboration.
Average customer rating:
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Risk Measurement, Econometrics and Neural Networks: Selected Articles of the 6th Econometric-Workshop in Karlsruhe, Germany (Contributions to Economics)
Manufacturer: Physica-Verlag Heidelberg
ProductGroup: Book
Binding: Paperback
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ASIN: 3790811521 |
Book Description
This book comprises the articles of the 6th Econometric Workshop in Karlsruhe, Germany. In the first part approaches from traditional econometrics and innovative methods from machine learning such as neural nets are applied to financial issues. Neural Networks are successfully applied to different areas such as debtor analysis, forecasting and corporate finance. In the second part various aspects from Value-at-Risk are discussed. The proceedings describe the legal framework, review the basics and discuss new approaches such as shortfall measures and credit risk.
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AI-based neural networks.: An article from: Business Perspectives
William E. Pracht
Manufacturer: University of Memphis
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ASIN: B00091WQYI
Release Date: 2005-07-28 |
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This digital document is an article from Business Perspectives, published by University of Memphis on March 22, 1990. The length of the article is 1594 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
Citation Details
Title: AI-based neural networks.
Author: William E. Pracht
Publication:
Business Perspectives (Refereed)
Date: March 22, 1990
Publisher: University of Memphis
Volume: v3
Issue: n3
Page: p14(3)
Distributed by Thomson Gale
Book Description
This digital document is an article from International Advances in Economic Research, published by Atlantic Economic Society on May 1, 1999. The length of the article is 6069 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
Citation Details
Title: Forecasting Economic Magnitudes with Neural Network Models.
Author: Agustin [*] Alonso-rodriguez
Publication:
International Advances in Economic Research (Refereed)
Date: May 1, 1999
Publisher: Atlantic Economic Society
Volume: 5
Issue: 2
Page: 215
Distributed by Thomson Gale
Book Description
This digital document is a journal article from Journal of Economics and Business, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Recent evidence suggests that oil prices affect output of the economy in a non-linear complex fashion. However, there is no clear agreement on the unknown functional form. An application of artificial neural network for short-term forecasting of GDP using oil prices and utilizing cascaded learning is proposed. We find that the mean absolute forecasting error and the mean square forecasting error is reduced by applying cascaded neural network relative to conventional artificial neural networks and popular linear models. Results also indicate that the developed forecasting approach is useful and point to the potential of this methodology for other economic applications. Our results are important for improving economic forecasts and introduce oil prices as a strong candidate for future forecasting exercises.
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Our Endangered Parks: What You Can Do to Protect Our National Heritage
National Parks and Conservation Association
Manufacturer: Foghorn Pr
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ASIN: 0935701842 |
Customer Reviews:
it sukt ballz.......1999-08-12
it was booring, and its a waste of my time
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