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010   $a3-319-23636-9
024 7 $a10.1007/978-3-319-23636-0
035   $a(CKB)3710000000498919
035   $a(EBL)4178547
035   $a(DE-He213)978-3-319-23636-0
035   $a(MiAaPQ)EBC4178547
035   $a(EXLCZ)993710000000498919
100   $a20151030d2016      u|             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aTechnical Analysis for Algorithmic Pattern Recognition$b[electronic resource] /$fby Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (213 p.)
300   $aDescription based upon print version of record.
311   $a3-319-23635-0 
320   $aIncludes bibliographical references at the end of each chapters.
327   $aTechnical 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.
330   $aThe 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.     .
606   $aFinance
606   $aEconometrics
606   $aStatistics 
606   $aPattern recognition
606   $aEconomics, Mathematical 
606   $aMacroeconomics
606   $aFinance, general$3https://scigraph.springernature.com/ontologies/product-market-codes/600000
606   $aEconometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/W29010
606   $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
606   $aQuantitative Finance$3https://scigraph.springernature.com/ontologies/product-market-codes/M13062
606   $aMacroeconomics/Monetary Economics//Financial Economics$3https://scigraph.springernature.com/ontologies/product-market-codes/W32000
615  0$aFinance.
615  0$aEconometrics.
615  0$aStatistics .
615  0$aPattern recognition.
615  0$aEconomics, Mathematical .
615  0$aMacroeconomics.
615 14$aFinance, general.
615 24$aEconometrics.
615 24$aStatistics for Business, Management, Economics, Finance, Insurance.
615 24$aPattern Recognition.
615 24$aQuantitative Finance.
615 24$aMacroeconomics/Monetary Economics//Financial Economics.
676   $a332
700   $aTsinaslanidis$b Prodromos E$4aut$4http://id.loc.gov/vocabulary/relators/aut$0982625
702   $aZapranis$b Achilleas D$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910254871503321
996   $aTechnical Analysis for Algorithmic Pattern Recognition$92242533
997   $aUNINA