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 text book 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”.
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