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Technical analysis for algorithmic p...

Tsinaslanidis, Prodromos E.

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Technical analysis for algorithmic pattern recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Technical analysis for algorithmic pattern recognition/ by Prodromos E. Tsinaslanidis, Achilleas D. Zapranis.
Author:	Tsinaslanidis, Prodromos E.
other author:	Zapranis, Achilleas D.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xiii, 204 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Technical analysis (Investment analysis) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-23636-0
ISBN:	9783319236360$q(electronic bk.)

Technical analysis for algorithmic pattern recognition
Tsinaslanidis, Prodromos E.

Technical analysis for algorithmic pattern recognition[electronic resource] /by Prodromos E. Tsinaslanidis, Achilleas D. Zapranis. - Cham :Springer International Publishing :2016. - xiii, 204 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783319236360$q(electronic bk.)

Standard No.: 10.1007/978-3-319-23636-0doiSubjects--Topical Terms:

2179653
Technical analysis (Investment analysis)

LC Class. No.: HG4529 / .T756 2016

Dewey Class. No.: 332.632042

Technical analysis for algorithmic pattern recognition
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100 1 $a Tsinaslanidis, Prodromos E. $3 2179651
245 1 0 $a Technical analysis for algorithmic pattern recognition $h [electronic resource] / $c by Prodromos E. Tsinaslanidis, Achilleas D. Zapranis.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xiii, 204 p. : $b ill., digital ; $c 24 cm.
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 Technical analysis (Investment analysis) $3 2179653
650 0 $a Pattern perception. $3 649387
650 1 4 $a Finance. $3 542899
650 2 4 $a Finance, general. $3 2162279
650 2 4 $a Econometrics. $3 542934
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 891081
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Quantitative Finance. $3 891090
650 2 4 $a Macroeconomics/Monetary Economics/Financial Economics. $3 2179654
700 1 $a Zapranis, Achilleas D. $3 2179652
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23636-0
950 $a Economics and Finance (Springer-41170)