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Technical Analysis for Algorithmic Pattern Recognition

  • Book
  • © 2016

Overview

Authors:
  1. Prodromos E. Tsinaslanidis

    1. The Business School, Canterbury Christ Church University, Canterbury, United Kingdom

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  2. Achilleas D. Zapranis

    1. Department of Accounting and Finance, University of Macedonia, Thessaloniki, Greece

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    You can also search for this author in PubMed   Google Scholar

  • Proposes unbiased, novel rule-based techniques for recognizing technical patterns
  • Implements a statistical framework for assessing realizing returns
  • Presents a unified methodological framework ?
  • Includes supplementary material: sn.pub/extras

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Technical Analysis

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 1-28

  3. Preprocessing Procedures

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 29-43

  4. Assessing the Predictive Performance of Technical Analysis

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 45-55

  5. Horizontal Patterns

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 57-83

  6. Zigzag Patterns

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 85-126

  7. Circular Patterns

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 127-145

  8. Technical Indicators

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 147-159

  9. A Statistical Assessment

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 161-192

  10. Dynamic Time Warping for Pattern Recognition

    • Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
    Pages 193-204
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Keywords

About this book

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.      ​

Authors and Affiliations

  • The Business School, Canterbury Christ Church University, Canterbury, United Kingdom

    Prodromos E. Tsinaslanidis

  • Department of Accounting and Finance, University of Macedonia, Thessaloniki, Greece

    Achilleas D. Zapranis

About the authors

Prodromos E. Tsinaslanidis, Ph.D., is Lecturer of Finance in the Business School at the Canterbury Christ Church University. Dr. Tsinaslanidis’ research interests include technical analysis, pattern recognition, efficient market hypothesis and design and assessment of investment and trading strategies.

Achilleas D. Zapranis, Ph.D., is Professor of Finance in the Department of Accounting and Finance at the University of Macedonia, where he is also Rector. In addition, Dr. Zapranis is a member of the Board of Directors of Thessaloniki’s Innovation Zone.


 


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