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Technical Analysis for Algorithmic Pattern Recognition

Authors: Tsinaslanidis, Prodromos E., Zapranis, Achilleas D.

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  • Proposes unbiased, novel rule-based techniques for recognizing technical patterns
  • Implements a statistical framework for assessing realizing returns
  • Presents a unified methodological framework ​
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eBook 83,29 €
price for Spain (gross)
  • ISBN 978-3-319-23636-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 103,99 €
price for Spain (gross)
  • ISBN 978-3-319-23635-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 103,99 €
price for Spain (gross)
  • ISBN 978-3-319-35395-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

The main purpose of this book is to resolve deficiencies and limitations that currently exist when using Technical Analysis (TA). Particularly, TA is being used either by academics as an “economic test” of the weak-form Efficient Market Hypothesis (EMH) or by practitioners as a main or supplementary tool for deriving trading signals. This book approaches TA in a systematic way utilizing all the available estimation theory and tests. This is achieved through the developing of novel rule-based pattern recognizers, and the implementation of statistical tests for assessing the importance of realized returns. More emphasis is given to technical patterns where subjectivity in their identification process is apparent. Our proposed methodology is based on the algorithmic and thus unbiased pattern recognition. The unified methodological framework presented in this book can serve as a benchmark for both future academic studies that test the null hypothesis of the weak-form EMH and for practitioners that want to embed TA within their trading/investment decision making processes.      ​

About the authors

Prodromos E. Tsinaslanidis, Ph.D., is Lecturer of Finance in the Business School at the Canterbury Christ Church University. Dr. Tsinaslanidis’ research interests include technical analysis, pattern recognition, efficient market hypothesis and design and assessment of investment and trading strategies.

Achilleas D. Zapranis, Ph.D., is Professor of Finance in the Department of Accounting and Finance at the University of Macedonia, where he is also Rector. In addition, Dr. Zapranis is a member of the Board of Directors of Thessaloniki’s Innovation Zone.


 


Table of contents (9 chapters)

Table of contents (9 chapters)

Buy this book

eBook 83,29 €
price for Spain (gross)
  • ISBN 978-3-319-23636-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 103,99 €
price for Spain (gross)
  • ISBN 978-3-319-23635-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 103,99 €
price for Spain (gross)
  • ISBN 978-3-319-35395-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Technical Analysis for Algorithmic Pattern Recognition
Authors
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-23636-0
DOI
10.1007/978-3-319-23636-0
Hardcover ISBN
978-3-319-23635-3
Softcover ISBN
978-3-319-35395-1
Edition Number
1
Number of Pages
XIV, 204
Topics