Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy

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Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy

Manufacturer: Storming Media
ProductGroup: Book
Binding: Spiral-bound
ASIN: 1423557301

Product Description

This is a NAVAL POSTGRADUATE SCHOOL MONTEREY CA report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A500553. The abstract provided by the Pentagon follows: This research examines and analyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of using neural networks as a forecasting tool for the individual investor. This study builds upon the work done by Edward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.

Neural Network Time Series: Forecasting of Financial Markets

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Better than I expected

Neural Network Time Series: Forecasting of Financial Markets
E. Michael Azoff
Manufacturer: John Wiley & Sons
ProductGroup: Book
Binding: Hardcover

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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.

Neural Networks for Financial Forecasting (Wiley Trading)

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.

Neural Networks for Financial Forecasting (Wiley Trading)
Edward Gately
Manufacturer: John Wiley & Sons
ProductGroup: Book
Binding: Hardcover

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General | Business & Investing | Subjects | Books

Futures | Investing | Business & Investing | Subjects | Books

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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.

Distribution forecasting of high frequency time series [An article from: Decision Support Systems]

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Distribution forecasting of high frequency time series [An article from: Decision Support Systems]
A. Pasley , and J. Austin
Manufacturer: Elsevier
ProductGroup: Book
Binding: Digital

Elsevier | By Publisher | e-Docs | Formats | Books
ASIN: B000RQYF8A

Book Description

Neural network approach to forecasting of quasiperiodic financial time series [An article from: European Journal of Operational Research]

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Neural network approach to forecasting of quasiperiodic financial time series [An article from: European Journal of Operational Research]
Y. Bodyanskiy , and S. Popov
Manufacturer: Elsevier
ProductGroup: Book
Binding: Digital
ASIN: B000PAUKDM

Book Description

This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2006. 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:
A novel neural network approach to forecasting of financial time series based on the presentation of the series as a combination of quasiperiodic components is presented. Separate components may have aliquant, and possibly non-stationary frequencies. All their parameters are estimated in real time in an ensemble of predictors, whose outputs are then optimally combined to obtain the final forecast. Special architecture of artificial neural network and learning algorithms implementing this approach are developed.

Neural network techniques for financial performance prediction: integrating fundamental and technical analysis [An article from: Decision Support Systems]

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Neural network techniques for financial performance prediction: integrating fundamental and technical analysis [An article from: Decision Support Systems]
M. Lam
Manufacturer: Elsevier
ProductGroup: Book
Binding: Digital

Book Description

This digital document is a journal article from Decision Support Systems, published by Elsevier in 2004. 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:
This research project investigates the ability of neural networks, specifically, the backpropagation algorithm, to integrate fundamental and technical analysis for financial performance prediction. The predictor attributes include 16 financial statement variables and 11 macroeconomic variables. The rate of return on common shareholders' equity is used as the to-be-predicted variable. Financial data of 364 S&P companies are extracted from the CompuStat database, and macroeconomic variables are extracted from the Citibase database for the study period of 1985-1995. Used as predictors in Experiments 1, 2, and 3 are the 1 year's, the 2 years', and the 3 years' financial data, respectively. Experiment 4 has 3 years' financial data and macroeconomic data as predictors. Moreover, in order to compensate for data noise and parameter misspecification as well as to reveal prediction logic and procedure, we apply a rule extraction technique to convert the connection weights from trained neural networks to symbolic classification rules. The performance of neural networks is compared with the average return from the top one-third returns in the market (maximum benchmark) that approximates the return from perfect information as well as with the overall market average return (minimum benchmark) that approximates the return from highly diversified portfolios. Paired t tests are carried out to calculate the statistical significance of mean differences. Experimental results indicate that neural networks using 1 year's or multiple years' financial data consistently and significantly outperform the minimum benchmark, but not the maximum benchmark. As for neural networks with both financial and macroeconomic predictors, they do not outperform the minimum or maximum benchmark in this study. The experimental results also show that the average return of 0.25398 from extracted rules is the only compatible result to the maximum benchmark of 0.2786. Consequentially, we demonstrate rule extraction as a postprocessing technique for improving prediction accuracy and for explaining the prediction logic to financial decision makers.

Intelligent Systems and Financial Forecasting

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
ProductGroup: Book
Binding: Paperback

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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.

Book Description

Get new employees up to speed quickly.

Staff Development: The Supervisor's Job (Department of Health, Education, and Welfare Training Manual No. 6)

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Staff Development: The Supervisor's Job (Department of Health, Education, and Welfare Training Manual No. 6)
Milton Hall
Manufacturer: U. S. Department of Health, Education, and Welfare, Division of Personnel Management, Staff Development Branch
ProductGroup: Book
Binding: Paperback

General | Business & Investing | Subjects | Books

Management | Management & Leadership | Business & Investing | Subjects | Books
ASIN: B0007FC2VI

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The Supervisor and On-The-Job Training
Martin Broadwell
Manufacturer: Basic Books
ProductGroup: Book
Binding: Paperback

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The Supervisor and On-The-Job Training
Martin M. Broadwell
Manufacturer: Addison-Wesley Publishing Co.
ProductGroup: Book
Binding: Paperback
ASIN: B000NP25KU

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The Supervisor and On-the-Job Training

Manufacturer: Addison-Wesley
ProductGroup: Book
Binding: Paperback
ASIN: B000F7GZTI

Book Description

Every day some of the most toxic poisons - poisons that eventually effect all life on earth - are sprayed on the foods we eat: apples, peaches, corn, grapes, and strawberries, to name a few. In eloquent words and pictures, One Good Apple makes a cogent, urgent case for healthier agricultural practices, exploring with precision and care the destructiveness of pesticides and fertilizers to everything from the smallest organisms to the water we drink. The author shows how, in our attempt to control nature, we are growing food that harms not only us but also the very balance of nature, a balance we need to better understand and protect. Here is a book to empower our next generation of gardeners and farmers by presenting a way of growing good food that nourishes not only us, but the earth as well.

Customer Reviews:

Perpetuates the DDT Myth.......2006-07-12

This book, sadly, perpetuates the myth that DDT is harmful. This book should not be bought and is hardly worth reading. It seems to be made for children, as there is not much content. I did see some interesting varieties of apples and tomatoes in the book, but that hardly makes it worth buying.

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