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Technical Analysis for Algorithmic Pattern Recognition

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 sup...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Tsinaslanidis, Prodromos E. (Autor), Zapranis, Achilleas D. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Temas:
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Technical Analysis for Algorithmic Pattern Recognition  |h [electronic resource] /  |c by Prodromos E. Tsinaslanidis, Achilleas D. Zapranis. 
250 |a 1st ed. 2016. 
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300 |a XIV, 204 p.  |b online resource. 
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505 0 |a 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. 
520 |a 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.     . 
650 0 |a Finance. 
650 0 |a Econometrics. 
650 0 |a Statistics . 
650 0 |a Pattern recognition systems. 
650 0 |a Social sciences-Mathematics. 
650 0 |a Macroeconomics. 
650 1 4 |a Financial Economics. 
650 2 4 |a Econometrics. 
650 2 4 |a Statistics in Business, Management, Economics, Finance, Insurance. 
650 2 4 |a Automated Pattern Recognition. 
650 2 4 |a Mathematics in Business, Economics and Finance. 
650 2 4 |a Macroeconomics and Monetary Economics. 
700 1 |a Zapranis, Achilleas D.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9783319236353 
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950 |a Economics and Finance (SpringerNature-41170) 
950 |a Economics and Finance (R0) (SpringerNature-43720)