大葉大學圖書館 |

Technical analysis for algorithmic pattern recognition[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	332.632042
書名/作者:	Technical analysis for algorithmic pattern recognition/ by Prodromos E. Tsinaslanidis, Achilleas D. Zapranis.
作者:	Tsinaslanidis, Prodromos E.
其他作者:	Zapranis, Achilleas D.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xiii, 204 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Econometrics.
標題:	Statistics for Business/Economics/Mathematical Finance/Insurance.
標題:	Pattern Recognition.
標題:	Quantitative Finance.
標題:	Macroeconomics/Monetary Economics/Financial Economics.
標題:	Technical analysis (Investment analysis)
標題:	Pattern perception.
標題:	Finance.
標題:	Finance, general.
ISBN:	9783319236360
ISBN:	9783319236353
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-23636-0

Technical analysis for algorithmic pattern recognition[electronic resource] /
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

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

186734
Econometrics.

LC Class. No.: HG4529 / .T756 2016

Dewey Class. No.: 332.632042

Technical analysis for algorithmic pattern recognition[electronic resource] /
LDR:02335nam a2200313 a 4500 001 454748
003 DE-He213
005 20160727144626.0
006 m d
007 cr nn 008maaau
008 161227s2016 gw s 0 eng d
020 $a 9783319236360 $q (electronic bk.)
020 $a 9783319236353 $q (paper)
024 7 $a 10.1007/978-3-319-23636-0 $2 doi
035 $a 978-3-319-23636-0
040 $a GP $c GP
041 0 $a eng
050 4 $a HG4529 $b .T756 2016
072 7 $a KFF $2 bicssc
072 7 $a BUS027000 $2 bisacsh
082 0 4 $a 332.632042 $2 23
090 $a HG4529 $b .T882 2016
100 1 $a Tsinaslanidis, Prodromos E. $3 652510
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 2 4 $a Econometrics. $3 186734
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 464928
650 2 4 $a Pattern Recognition. $3 463916
650 2 4 $a Quantitative Finance. $3 464461
650 2 4 $a Macroeconomics/Monetary Economics/Financial Economics. $3 639425
650 0 $a Technical analysis (Investment analysis) $3 367132
650 0 $a Pattern perception. $3 205660
650 1 4 $a Finance. $3 189642
650 2 4 $a Finance, general. $3 634722
700 1 $a Zapranis, Achilleas D. $3 652511
710 2 $a SpringerLink (Online service) $3 463450
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)