Technical Analysis for Algorithmic Pattern Recognition

✍ Scribed by Prodromos E. Tsinaslanidis, Achilleas D. Zapranis (auth.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 213
• Edition: 1
• Category: Library

✦ Synopsis

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

Front Matter....Pages i-xiii
Technical Analysis....Pages 1-28
Preprocessing Procedures....Pages 29-43
Assessing the Predictive Performance of Technical Analysis....Pages 45-55
Horizontal Patterns....Pages 57-83
Zigzag Patterns....Pages 85-126
Circular Patterns....Pages 127-145
Technical Indicators....Pages 147-159
A Statistical Assessment....Pages 161-192
Dynamic Time Warping for Pattern Recognition....Pages 193-204

✦ Subjects

Finance, general; Econometrics; Statistics for Business/Economics/Mathematical Finance/Insurance; Pattern Recognition; Quantitative Finance; Macroeconomics/Monetary Economics//Financial Economics