Technical Analysis for Algorithmic Pattern Recognition - Hardcover

Tsinaslanidis, Prodromos E.; Zapranis, Achilleas D.

 
9783319236353: Technical Analysis for Algorithmic Pattern Recognition
Hardcover
ISBN 10: 3319236350 ISBN 13: 9783319236353
Publisher: Springer, 2015

Synopsis

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.      ​

"synopsis" may belong to another edition of this title.

About the Author

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.


 


From the Back Cover

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 this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2015
Language
English
ISBN 10 
3319236350
ISBN 13 
9783319236353
Binding
Hardcover
Edition number
1
Number of pages
218

Other Popular Editions of the Same Title

9783319353951: Technical Analysis for Algorithmic Pattern Recognition

Featured Edition

ISBN 10:  3319353950 ISBN 13:  9783319353951
Publisher: Springer, 2016
Softcover