Technical Analysis for Algorithmic Pattern Recognition

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.

Autorentext

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.

Inhalt
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.