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Record Nr. |
UNINA9910254871503321 |
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Autore |
Tsinaslanidis Prodromos E |
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Titolo |
Technical Analysis for Algorithmic Pattern Recognition [[electronic resource] /] / by Prodromos E. Tsinaslanidis, Achilleas D. Zapranis |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (213 p.) |
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Disciplina |
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Soggetti |
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Finance |
Econometrics |
Statistics |
Pattern recognition |
Economics, Mathematical |
Macroeconomics |
Finance, general |
Statistics for Business, Management, Economics, Finance, Insurance |
Pattern Recognition |
Quantitative Finance |
Macroeconomics/Monetary Economics//Financial Economics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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