Technical Analysis for Algorithmic Pattern Recognition
Autor Prodromos E. Tsinaslanidis, Achilleas D. Zapranisen Limba Engleză Hardback – 6 noi 2015
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Specificații
ISBN-13: 9783319236353
ISBN-10: 3319236350
Pagini: 210
Ilustrații: XIV, 204 p.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.49 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3319236350
Pagini: 210
Ilustrații: XIV, 204 p.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.49 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Public țintă
ResearchCuprins
Technical Analysis.- Preprocessing Procedures.- Assessing the Predictive Performance of Technical Analysis.- Horizontal Patterns.- Zigzag Patterns.- Circular Patterns.- Technical Indicators.- A Statistical Assessment.- Dynamic Time Warping for Pattern Recognition.
Notă biografică
Prodromos E. Tsinaslanidis, Ph.D., isLecturer of Finance in the Business School at the Canterbury Christ ChurchUniversity. Dr. Tsinaslanidis’ research interests include technical analysis,pattern recognition, efficient market hypothesis and design and assessment ofinvestment and trading strategies.
Achilleas D.Zapranis, Ph.D., isProfessor of Finance in the Department of Accounting and Finance at theUniversity of Macedonia, where he is also Rector. In addition, Dr. Zapranis isa member of the Board of Directors of Thessaloniki’s Innovation Zone.
Achilleas D.Zapranis, Ph.D., isProfessor of Finance in the Department of Accounting and Finance at theUniversity of Macedonia, where he is also Rector. In addition, Dr. Zapranis isa member of the Board of Directors of Thessaloniki’s Innovation Zone.
Textul de pe ultima copertă
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.
Caracteristici
Proposes unbiased, novel rule-based techniques for recognizing technical patterns Implements a statistical framework for assessing realizing returns Presents a unified methodological framework ? Includes supplementary material: sn.pub/extras