Computational Intelligence Techniques for Trading and Investment

by Christian Dunis (Editor), Spiros Likothanassis (Editor), Andreas Karathanasopoulos (Editor), Georgios Sermpinis (Editor), Konstantinos Theofilatos (Editor)

Overview

Computational intelligence, a sub-branch of artificial intelligence, is a field which draws on the natural world and adaptive mechanisms in order to study behaviour in changing complex environments. This book provides an interdisciplinary view of current technological advances and challenges concerning the application of computational intelligence techniques to financial time-series forecasting, trading and investment.

The book is divided into five parts. The first part introduces the most important computational intelligence and financial trading concepts, while also presenting the most important methodologies from these different domains. The second part is devoted to the application of traditional computational intelligence techniques to the fields of financial forecasting and trading, and the third part explores the applications of artificial neural networks in these domains. The fourth part delves into novel evolutionary-based hybrid methodologies for trading and portfolio management, while the fifth part presents the applications of advanced computational intelligence modelling techniques in financial forecasting and trading.

This volume will be useful for graduate and postgraduate students of finance, computational finance, financial engineering and computer science. Practitioners, traders and financial analysts will also benefit from this book.

Product Details

ISBN-13:	9780415636803
Publisher:	Taylor & Francis
Publication date:	03/06/2014
Series:	Routledge Advances in Experimental and Computable Economics , #6
Pages:	238
Product dimensions:	6.12(w) x 9.19(h) x (d)

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