Technical Analysis for Algorithmic Pattern Recognition - Rilegato

Tsinaslanidis, Prodromos E.; Zapranis, Achilleas D.

 
9783319236353: Technical Analysis for Algorithmic Pattern Recognition
Rilegato
ISBN 10: 3319236350 ISBN 13: 9783319236353
Casa editrice: Springer Nature, 2015

Sinossi

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. ?

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

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.


 


Dalla quarta di copertina

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. ?

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Nature
Data di pubblicazione
2015
Lingua
Inglese
ISBN 10 
3319236350
ISBN 13 
9783319236353
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
220
Contatto del produttore
non disponibile
Persona responsabile
CMN Printpool Inh. Mathis Neumann
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Altre edizioni note dello stesso titolo

9783319353951: Technical Analysis for Algorithmic Pattern Recognition

Edizione in evidenza

ISBN 10:  3319353950 ISBN 13:  9783319353951
Casa editrice: Springer, 2016
Brossura