Título :	Technical Analysis for Algorithmic Pattern Recognition
Tipo de documento:	documento electrónico
Autores:	Tsinaslanidis, Prodromos E ; Zapranis, Achilleas D ; SpringerLink (Online service)
Editorial:	Cham : Springer International Publishing
Fecha de publicación:	2016
Otro editor:	Imprint: Springer
Número de páginas:	XIII, 204 p. 55 illus., 26 illus. in color
Il.:	online resource
ISBN/ISSN/DL:	978-3-319-23636-0
Idioma :	Inglés (eng)
Palabras clave:	Finance  Pattern  recognition  Economics,  Mathematical  Statistics  Econometrics  Macroeconomics  Finance,  general  for  Business/Economics/Mathematical  Finance/Insurance  Recognition  Quantitative  Macroeconomics/Monetary  Economics//Financial  Economics
Clasificación:	330 Economía en general
Resumen:	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.
Nota de contenido:	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
En línea:	http://dx.doi.org/10.1007/978-3-319-23636-0
Link:	https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=42160

Technical Analysis for Algorithmic Pattern Recognition [documento electrónico] / Tsinaslanidis, Prodromos E ; Zapranis, Achilleas D ; SpringerLink (Online service) . - Cham : Springer International Publishing : Imprint: Springer, 2016 . - XIII, 204 p. 55 illus., 26 illus. in color : online resource.
ISBN : 978-3-319-23636-0
Idioma : Inglés (eng)

Palabras clave:	Finance  Pattern  recognition  Economics,  Mathematical  Statistics  Econometrics  Macroeconomics  Finance,  general  for  Business/Economics/Mathematical  Finance/Insurance  Recognition  Quantitative  Macroeconomics/Monetary  Economics//Financial  Economics
Clasificación:	330 Economía en general
Resumen:	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.
Nota de contenido:	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
En línea:	http://dx.doi.org/10.1007/978-3-319-23636-0
Link:	https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=42160