rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Technical Analysis for Algorithmic Pattern Recognition
  name:
    name part: Tsinaslanidis, Prodromos E.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Zapranis, Achilleas D.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 204 p. online resource.
  abstract: 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. .
  table of contents: 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.
  note: by Prodromos E. Tsinaslanidis, Achilleas D. Zapranis.
  subject:
    topic: Finance
  subject:
    topic: Pattern recognition
  subject:
    topic: Economics, Mathematical
  subject:
    topic: Statistics
  subject:
    topic: Econometrics
  subject:
    topic: Macroeconomics
  subject:
    topic: Finance
  subject:
    topic: Finance, general
  subject:
    topic: Econometrics
  subject:
    topic: Statistics for Business/Economics/Mathematical Finance/Insurance
  subject:
    topic: Pattern Recognition
  subject:
    topic: Quantitative Finance
  subject:
    topic: Macroeconomics/Monetary Economics//Financial Economics
  classification: HG1-HG9999
  classification: 332
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319236360
  identifier: http://dx.doi.org/10.1007/978-3-319-23636-0
  record info:
    record creation date: 151030
    record change date: 20170520141602.0
    record identifier: 978-3-319-23636-0