Computational Intelligence Techniques for Trading and Investment

Computational Intelligence Techniques for Trading and Investment

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Product Details

ISBN-13: 9781136195105
Publisher: Taylor & Francis
Publication date: 03/26/2014
Series: Routledge Advances in Experimental and Computable Economics
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 218
File size: 4 MB

About the Author

Christian Dunis is Emeritus Professor of Banking and Finance at Liverpool John Moores University, UK and Joint General Manager of global risk and new products at Horus Partners Wealth Management Group SA, Switzerland.

Spiros Likothanassis is Professor and Director at the Pattern Recognition Laboratory in the Department of Computer Engineering and Informatics at the University of Patras, Greece.

Andreas Karathanasopoulos is Senior Lecturer in Finance and Risk Management at the University of East London, UK

Georgios Sermpinis is Senior Lecturer in Economics at the University of Glasgow, UK.

Konstantinos Theofilatos is a Post-Doctoral Researcher in the Department of Computer Engineering and Informatics at the University of Patras, Greece.

Table of Contents

Part I: Introduction  1. Computational Intelligence: Recent advances, perspectives and open problems K. Theofilatos, E. Georgopoulos, S. Likothanassis and S. Mavroudi  2. Forecasting and trading strategies: A survey C. Stasinakis and G. Sermpinis  Part II: Trading and Investments with Traditional Computational Intelligence Techniques  3. Hidden Markov Models: Financial modelling and applications S. Mitra  4. Modelling and Trading Financial Time-Series Using Kalman Filters and ARMA Modelling C. Dimitrakopoulos, A. Karathanasopoulos, G. Sermpinis and S. Likothanassis  Part III: Trading and Investments with Artificial Neural Networks  5. Modelling and Trading the Corn/Ethanol Crush Spread with Neural Networks C. Dunis, J. Laws, P. Middleton and A. Karathanasopoulos  6. Trading Decision Support with Historically Consistent Neural Networks J. Von Mettenheim  Part IV: Trading and Investments with Hybrid Evolutionary Methodologies  7. Modelling and Trading Financial Time-Series Using Adaptive Evolutionary Neural Networks K. Theofilatos, T. Amorgianiotis, A. Karathanasopoulos, G. Sermpinis and S. Likothanassis  8. Portfolio Construction Using Argumentation and Hybrid Evolutionary Forecasting Algorithms N. Spanoudakis, K. Pendaraki and G. Beligiannis  Part V: Trading and Investments with Advanced Computational Intelligence Modelling Techniques  9. Forecasting DAX30 Using Support Vector Machine and VDAX R. Rosillo, J. Giner and D. de la Fuente  10. Ensemble Learning of High-Dimensional Stock Market Data M. Maragoudakis and D. Serpanos

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