Technical Analysis for Algorithmic Pattern Recognition
(Sprache: Englisch)
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...
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Klappentext zu „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.
Inhaltsverzeichnis zu „Technical Analysis for Algorithmic Pattern Recognition “
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.
Autoren-Porträt von Prodromos E. Tsinaslanidis, Achilleas D. Zapranis
Dr. Prodromos E. Tsinaslanidis holds a Ph.D. in finance from the Department of Accounting and Finance at the University of Macedonia, Greece (2012), a MSc (2007) and a BSc (2005) in Accounting and Finance from the University of Macedonia, Greece, and a MSc degree in Statistics and Modeling (2013) from the School of Mathematics at the Aristotelian University of Thessaloniki, Greece. In 2006 he was certified as Market Maker/Market Trader by the Athens Stock Exchange. Since 2010 he is an Adjunct Lecturer of Finance, at the University of Macedonia of Economic and Social Sciences. He has also served as a finance instructor for the economic chamber of Greece. His research interest are close related to technical analysis, pattern recognition, efficient market hypothesis, financial markets, artificial intelligence and computer science. So far he has published several papers in international scientific journals, in contributed volumes and in international conferences.Dr. Achilleas D. Zapranis is Associate Professor of Finance and Vice-Rector of Economic Programming and Development of the University of Macedonia of Economic and Social Sciences, Greece. He holds a Ph.D. in Decision Science from London Business School (1997), a Master's degree in Computer Science from University College London (1992) and a degree in Mechanical Engineering from Aristotelian University, Thessaloniki (1989). He has authored three monographs on the subject of neural modeling in financial/business applications (Klidarithmos 2005, Springer-Verlag in 1999-the latter was co-authored with Professor A.-P. Refenes); Weather Derivatives (Springer-Verlag in 2012, co-authored with Dr. A. K. Alexandridis) and two monographs in financial applications with matlab and financial risk management (Klidarithmos 2009 and Klidarithmos 2009-the first was co-authored with Lecturer E. Livanis).
Bibliographische Angaben
- Autoren: Prodromos E. Tsinaslanidis , Achilleas D. Zapranis
- 2016, 1st ed. 2016, XIV, 204 Seiten, Masse: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3319236350
- ISBN-13: 9783319236353
- Erscheinungsdatum: 06.11.2015
Sprache:
Englisch
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