ACIFF 2013 - 2nd International Workshop on Applying Computational Intelligence Techniques in Financial Time Series Forecasting and Trading

Computational Intelligence ? a sub-branch of Artificial Intelligence ? studies are primarily inspired by the laws of nature and adaptive mechanisms in order to enable or facilitate intelligent behavior in changing complex environments. These mechanisms include these Artificial Intelligence paradigms that exhibit an ability to learn and adapt to new situations, to generalize, abstract and discover new knowledge. The following paradigms are commonly associated with Computational Intelligence: artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems. Individual techniques from these Computational Intelligence paradigms, as well as combinations (hybrid methods) of these paradigms, have been applied successfully to solve a variety of real world problems. In particular, these techniques have been widely applied to time series prediction, financial forecasting and trading.
The aim of this workshop is to serve as an interdisciplinary forum for bringing together specialists from the scientific areas of Computer Engineering, Finance and Operational Research. The focus of this workshop is on current technological advances and challenges about the applications of computational intelligence techniques in financial time-series forecasting and trading.
Therefore, the Workshop on “Applying Computational Intelligence Techniques in Financial Time Series Forecasting and Trading” will welcome paper submissions introducing and implementing Computational Intelligent techniques to address various modeling and predicting financial applications. This workshop will provide a medium for the exchange of ideas between theoreticians and practitioners.
Topics of interest
Topics of interest include, but are not limited to, the use of techniques like:
Traditional and Statistical Techniques
Autoregressive Moving Average (ARMA) techniques
Moving Average (MA) techniques
Fundamental trading techniques and strategies
Regressive Forecasting
Volatility based trading
Bayesian and likelihood based techniques
Artificial Neural Networks
Multi Layer Perceptrons
Higher Order Neural Networks
Recurrent Neural Networks
Radial Basis Function Neural Networks
Support Vector Machines
Evolutionary Techniques
Genetic Programming
Gene Expression Programming
Linear Genetic Programming
Evolutionary Strategies
Genetic Algorithm based hybrid techniques
Advanced and/or Hybrid Techniques
Random Trees ? Random Forests
Fuzzy Logic based Techniques
Ensemble Techniques
Hybrid Techniques
Swarm Intelligence and Differential Evolution based Hybrid Techniques
Workshop Chairs
Spiridon D. Likothanassis, University of Patras, Greece likothan-AT-ceid.upatras.gr
Efstratios F. Georgopoulos, Technological Educational Institute (T.E.I.) of Kalamata, Greece sfg-AT-teikal.gr
Georgios Sermpinis, University of Glasgow, Scotland georgios.sermpinis-AT-glasgow.ac.uk
Andreas S. Karathanasopoulos, London Metropolitan University, UK a.karathanasopoulos-AT-londonmet.ac.uk
Konstantinos Theofilatos, University of Patras, Greece theofilk-AT-ceid.upatras.gr
Academic Program Committee
Spiridon D. Likothanassis, University of Patras, Greece
Christian Dunis, Liverpool John Moores University, UK
Hans-Jörg von Mettenheim, Institut für Wirtschaftsinformatik, Germany
Efstratios F. Georgopoulos, Technological Educational Institute (T.E.I.) of Kalamata, Greece
Georgios Sermpinis, University of Glasgow, Scotland
Andreas S. Karathanasopoulos, London Metropolitan University, UK
Rafael Rosillo, University of Oviedo, Spain
Andreas Andreou, Cyprus University of Technology, Cyprus
Grigorios Beligiannis, University of Western Greece, Greece
Harris Papadopoulos, Frederick University, Cyprus
Efi Papatheocharous, University of Cyprus, Cyprus