Logo - springer
Slogan - springer

Engineering - Computational Intelligence and Complexity | Investment Strategies Optimization based on a SAX-GA Methodology

Investment Strategies Optimization based on a SAX-GA Methodology

Canelas, António M.L., Neves, Rui F.M.F., Horta, Nuno C G

2013, XII, 81 p. 81 illus., 19 illus. in color.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$39.95

(net) price for USA

ISBN 978-3-642-33110-7

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase


learn more about Springer eBooks

add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$49.95

(net) price for USA

ISBN 978-3-642-33109-1

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • Presents a new computational finance approach combining SAX and GA
  • Shows soft computing and computational intelligence as solutions for financial markets
  • Case studies presented help identifying the investment strategy to apply in different situations
This book presents a new computational finance approach combining a Symbolic Aggregate approXimation (SAX) technique with an optimization kernel based on genetic algorithms (GA). While the SAX representation is used to describe the financial time series, the evolutionary optimization kernel is used in order to identify the most relevant patterns and generate investment rules. The proposed approach considers several different chromosomes structures in order to achieve better results on the trading platform The methodology presented in this book has great potential on investment markets.

Content Level » Research

Keywords » Financial Market - Frequent Patterns - Genetic Algorithm - Pattern Discovery - Pattern Recognition - SAX Representation

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity - Financial Economics - Quantitative Finance

Table of contents / Preface / Sample pages 

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Computational Intelligence.