Andreas Karathanasopoulos is a Professor of Business Analytics and Forecasting Financial markets at the University of Dubai Business School. His main qualification is the prediction of financial markets with lineal and nonlinear models. Previously he held several positions at the American University of Beirut, at the South Oriental African studies London University and at the Ulster University in Northern Ireland. 
Andreas received his Master of Science from Liverpool John Moores University. His Msc dissertation was on forecasting the Greek stock Market volatility with Hybrid Neural Networks. Further to that he completed his PhD studies in same university and the thesis topic was the prediction of the Greek stock market with new artificial intelligent techniques. He developed new forecasting models by applying them in Greek stock indices and in Greek corporate bonds. 
Andreas has published in a diverse range of top-tier journals including the European Journal of Operational Research, journal of computational economics , review of quantitative finance and accounting, European journal of finance, journal of quantitative finance, expert system with applications, computational finance , journal of forecasting, economic letters, applied economics, applied economic letters, journal of asset management, journal of investing , journal of hospitality and tourism management, energy economics and theoretical economic letters. 
Moreover, Andreas is the author of artificial intelligence in financial markets, published from Palgrave Macmillan, he is presenting cutting edge applications for risk management, portfolio optimization and economics. The aim of this book is to focus on Artificial Intelligence and to provide broad examples of its application to the field of finance. Due to the popularity and rapid emergence of AI in the area of finance this book is the first volume in a series called ‘New Developments in Quantitative Trading and Investment’ Moreover, this particular volume targets a wide audience including both academic and professional financial analysts. The content of this textbook targets a wide audience who are interested in forecasting, modelling, trading, risk management, economics, credit risk and portfolio management. 
In addition to the previous book Andreas has authored and a second book titled computational intelligence technique for trading and investment in collaboration with Routledge publishers. Computational intelligence, a sub-branch of artificial intelligence, is a field which draws on the natural world and adaptive mechanisms in order to study behavior 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. 
Finally, Andreas Karathanasopoulos conducts research on the development of novel statistical methodologies and its applications to large scale datasets in energy, stock markets and macroeconomics. Except from that he is working on the construction of new artificial intelligent techniques in forecasting with more accuracy and efficiency, oil prices, electricity consumption indices, poverty indices and stock markets crashes. All these novelties has been applied successfully in the UAE market and in GCC in general. Last findings has been published in journal of forecasting in the research paper entitled “Forecasting the Dubai Financial Market with a Combination of Momentum Effect with a Deep Belief Network”.

Investment, quantitative finance, trading strategies, econometrics, forecasting financial markets. Expertise Forecasting, Quantitative analysis, econometrics and financial markets.

Modeling and trading financial markets.

Refereed Journals

• Lo, CC., Skindilias, K., and Karathanasopoulos, A. 2016. "Forecasting Latent Volatility through a Markov Chain Approximation Filter", JOURNAL OF FORECASTING, vol.35, no.1, pp-54-69
• Dunis, CL., et al. 2015. "Trading and hedging the corn/ethanol crush spread using time-varying leverage and nonlinear models", EUROPEAN JOURNAL OF FINANCE, vol.21, no.4, pp-352-375
• Mitra, S., et al. 2015. "Operational risk: Emerging markets, sectors and measurement", EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol.241, no.1, pp-122-132
• Sermpinis, G., et al. 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms-Support vector regression forecast combinations", EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol.247, no.3, pp-831-846
• Karathanasopoulos, A., et al. 2015. "Stock market prediction using evolutionary support vector machines: an application to the ASE20 index", European Journal of Finance
• Stasinakis, C., et al. 2014. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions", Computational Economics
• Sermpinis, G., et al. 2014. "Inflation and Unemployment Forecasting with Genetic Support Vector Regression", JOURNAL OF FORECASTING, vol.33, no.6, pp-471-487
• Karathanasopoulos, A., et al. 2014. "Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms", JOURNAL OF FORECASTING, vol.33, no.8, pp-596-610
• Theofilatos, K., et al. 2013. "Modeling and Trading FTSE100 Index Using a Novel Sliding Window Approach Which Combines Adaptive Differential Evolution and Support Vector Regression", ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2013, vol.412, pp-486-496
• Dunis, CL., Laws, J., and Karathanasopoulos, A. 2013. "GP algorithm versus hybrid and mixed neural networks", EUROPEAN JOURNAL OF FINANCE, vol.19, no.3, pp-180-205
• Sermpinis, G., et al. 2013. "Gene Expression Programming and Trading Strategies", ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2013, vol.412, pp-497-505
• Sermpinis, G., et al. 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization", EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol.225, no.3, pp-528-540
• Sermpinis, G., et al. 2013. "Kalman Filter and SVR Combinations in Forecasting US Unemployment", ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2013, vol.412, pp-506-515
• Vasilakis, G.A., et al. 2013. "A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading", Computational Economics, vol.42, no.4, pp-415-431
• Dunis, C.L., et al. 2013. "A hybrid genetic algorithm-support vector machine approach in the task of forecasting and trading", Journal of Asset Management, vol.14, no.1, pp-52-71
• Sermpinis, G., et al. 2012. "A Hybrid Radial Basis Function and Particle Swarm Optimization Neural Network Approach in Forecasting the EUR/GBP Exchange Rates Returns", ENGINEERING APPLICATIONS OF NEURAL NETWORKS, vol.311, pp-413-422 Sermpinis, G., et al. 2012. "Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks", EXPERT SYSTEMS WITH APPLICATIONS, vol.39, no.10, pp-8865-8877
• Theofilatos, K., et al. 2012. "Modelling and Trading the DJIA Financial Index Using Neural Networks Optimized with Adaptive Evolutionary Algorithms", ENGINEERING APPLICATIONS OF NEURAL NETWORKS, vol.311, pp-453-462
• Karathanasopoulos, AS., et al. 2010. "Modeling the Ase 20 Greek Index Using Artificial Neural Nerworks Combined with Genetic Algorithms", ARTIFICIAL NEURAL NETWORKS-ICANN 2010, PT I, vol.6352, pp-428-435

Books and Book Chapters

• Karathanasopoulos, A., et al. "Artificial Intelligence in Financial Markets. Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics"2016
• Theofilatos, K., et al. "Advanced short-term forecasting and trading deploying neural networks optimized with an adaptive evolutionary algorithm", Computational Intelligence Techniques for Trading and Investment2014
• Dunis, C., et al. "Computational intelligence techniques for trading and investment", Computational Intelligence Techniques for Trading and Investment2014
• Dunis, C.L., et al. "Modelling and trading the corn-ethanol crush spread with neural networks", Computational Intelligence Techniques for Trading and Investment2014
• Dimitrakopoulos, C., et al. "Adaptive filtering on forecasting financial derivatives indices", Computational Intelligence Techniques for Trading and Investment2014