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Genetic Algorithms Investment: Alternative Approach to Neural Networks and Chaos Theory (Wiley Finance) Hardcover – 7 Feb 1994

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Product details

  • Hardcover: 320 pages
  • Publisher: John Wiley & Sons, Inc. (7 Feb. 1994)
  • Language: English
  • ISBN-10: 0471576794
  • ISBN-13: 978-0471576792
  • Product Dimensions: 15.7 x 2.3 x 23.6 cm
  • Average Customer Review: 2.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 1,859,829 in Books (See Top 100 in Books)
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Product Description

From the Inside Flap

Genetic Algorithms and Investment Strategies More and more traders now rely on genetic algorithms, neural networks, chaos theory, and other computerized decision–making approaches to help them develop winning investment strategies. They recognize that the battle in today’s financial markets is increasingly being waged at computer speed, not human speed, and that anyone who fails to exploit these new tools may be headed for extinction. Written by the coauthor of the first published paper to link genetic algorithms and the world of finance, Richard Bauer’s Genetic Algorithms and Investment Strategies is, likewise, the first book to demonstrate the value of GAs as tools in the search for effective trading ideas. Recognizing the continued resistance of many traders and analysts to GAs, it shows how these approaches do not herald an age in which people will be supplanted by machines, revealing instead how they serve only to augment human thinking. In clear, nontechnical language, Genetic Algorithms and Investment Strategies describes the biological bases of GAs, neural nets, and chaos theory their historical development the current state of the methodology for each and their uses, advantages, and limitations as decision–making tools in the world of finance, ultimately demonstrating the superiority of GAs over the other two methods, particularly in handling critical optimization problems. It then focuses exclusively on GAs, presenting simple problems to illustrate the basic steps involved in using a GA and describing with the help of numerous tables and diagrams how the GA mimics nature’s ruthlessly efficient evolutionary process and moves quickly and inexorably toward a near–optimal solution. Complete with a summary of available software programs, an extensive glossary of GA terms, and a bibliography covering GAs, neural nets, chaos theory, and market timing, Genetic Algorithms and Investment Strategies doesn’t offer a results–oriented, get–rich–quick scheme. Rather, it provides traders and investment analysts with a proven, strategic decision–making process they can use and modify in order to prevail in today’s fast–shifting financial marketplace.

From the Back Cover

When you combine nature’s efficiency and the computer’s speed, the financial possibilities are almost limitless. Today’s traders and investment analysts require faster, sleeker weaponry in today’s ruthless financial marketplace. Battles are now waged at computer speed, with skirmishes lasting not days or weeks, but mere hours. In his series of influential articles, Richard Bauer has shown why these professionals must add new computerized decision–making tools to their arsenal if they are to succeed. In Genetic Algorithms and Investment Strategies, he uniquely focuses on the most powerful weapon of all, revealing how the speed, power, and flexibility of GAs can help them consistently devise winning investment strategies. The only book to demonstrate how GAs can work effectively in the world of finance, it first describes the biological and historical bases of GAs as well as other computerized approaches such as neural networks and chaos theory. It goes on to compare their uses, advantages, and overall superiority of GAs. In subsequently presenting a basic optimization problem, Genetic Algorithms and Investment Strategies outlines the essential steps involved in using a GA and shows how it mimics nature’s evolutionary process by moving quickly toward a near–optimal solution. Introduced to advanced variations of essential GA procedures, readers soon learn how GAs can be used to:
  • Solve large, complex problems and smaller sets of problems
  • Serve the needs of traders with widely different investment philosophies
  • Develop sound market timing trading rules in the stock and bond markets
  • Select profitable individual stocks and bonds
  • Devise powerful portfolio management systems
Complete with information on relevant software programs, a glossary of GA terminology, and an extensive bibliography covering computerized approaches and market timing, Genetic Algorithms and Investment Strategies unveils in clear, nontechnical language a remarkably efficient strategic decision–making process that, when imaginatively used, enables traders and investment analysts to reap significant financial rewards.

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Most Helpful Customer Reviews

2 of 2 people found the following review helpful By A Customer on 10 Mar. 1998
Format: Hardcover
The author presents a general introduction into ga's , then moves over to investment strategies and presents solutions.
Unfortunately, he does NOT give background information on the really interesting things like string patterns used, crossover and fitness function and the like. Futhermore, more than one third of the book is filled with endless tables whose content the reader understands after the first table. I guess it makes for a larger book.

conclusion: very disappointing.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 4 reviews
24 of 27 people found the following review helpful
Does not provide what it promises to 10 Mar. 1998
By A Customer - Published on Amazon.com
Format: Hardcover
The author presents a general introduction into ga's , then moves over to investment strategies and presents solutions.
Unfortunately, he does NOT give background information on the really interesting things like string patterns used, crossover and fitness function and the like. Futhermore, more than one third of the book is filled with endless tables whose content the reader understands after the first table. I guess it makes for a larger book.

conclusion: very disappointing.
5 of 5 people found the following review helpful
Good book for decision maker, bad book for the algorithm designer 11 Mar. 2007
By William Leizerowicz - Published on Amazon.com
Format: Hardcover
For a project decision maker trying to evaluate the genetic algorithm approach, this book helps to understand the main concepts. It is a great for anyone who wants ideas to help evaluate the potentials and shortcomings of a GA system without getting mixed up in the math and details. It includes comparisons to chaos theory and neural nets.

For programmers looking implement code, or data administrators looking for the right data feeds, however, this book might be a frustrating tease. The book is full of examples such as mentioning the selection of "10 parameters from a possible 150 macroeconomic variables", but never says what the parameters are. The book is still useful. A little patience with this book might yield some great ideas although the author didn't directly communicate it. Never the less, just one small idea could easily pay for this book many many times over. For the pure IT developer, I highly recommend "Neural Networks in Finance and Investing" by Trippi & Turban instead which includes documented code and examples on a disk.
2 of 2 people found the following review helpful
Héctor Rico 15 Jan. 2008
By Hector Rico Perez - Published on Amazon.com
Format: Hardcover
If you are looking for practical applications of this kind of algoritmicts do not buy this book. The 50% of the book are data tables, it would be better buying a cd-rom with all those data and the book had 200 less pages. A better choice than this book is "Trading on the edge" or "Neural networks for finance".
5 of 7 people found the following review helpful
A worthy introduction complete with examples 30 Dec. 2003
By dean_from_sa - Published on Amazon.com
Format: Hardcover
After I read this book I read the review from 1998. I was quite surprised that the prior reviewer felt that way. The book provides an introduction to developing the data required for testing, a methodology for developing a study that would be useful in investment practice. This is a quality effort. This book will not provide (or promise) the reader a turnkey system for conquering the market, however there is a framework for future research.
This book was written in 1994, before many of the books dealing with Neural Networks came out, and so the terminology will seem unfamiliar. If you are willing to work through these differences (and it is not too hard) then there is a great deal to learn here.
Bauer predicted (in 1994) that Genetic Algorithms would become widely used. Bauer also predicted that much of the development would be done in secret. I have not come across them in the last few years and at very least I would have expected to see them as signals for sale from system developers. Additionally, there are a series of books like this one that should have appeared since 1994. A search of Amazon using Genetic Algorithm as the subject and sorting by publication date returns 133 titles. I reviewed these titles and did not find any further investment focused titles. I will use this book as a starting point for my research.
My next book will be Melanie Mitchell's "Introduction to GAs".
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