Christian Dunis – författare

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.

Today s financial markets are characterised by a large number ofparticipants, with different appetites for risk, different timehorizons, different motivations and reactions to unexpected news.The mathematical techniques and models used in the forecasting offinancial markets have therefore grown ever more sophisticated astraders, analysts and investors seek to gain an edge on theircompetitors. Written by leading international researchers andpractitioners, this book focuses on three major themes of today sstate of the art financial research: modelling with high frequencydata, the information content of volatility markets, andapplications of neural networks and genetic algorithms to financialtime series. Forecasting Financial Markets includes empiricalapplications to present the very latest thinking on these complextechniques, including:* High frequency exchange rates * Intraday volatility * Autocorrelation and variance ratio tests * Conditional volatility * GARCH processes * Chaotic systems * Nonlinearity * Stochastic and EXPAR models * Artificial neural networks * Genetic algorithms

This text contains selected articles which, for the most part, were presented at the "Forecasting Financial Markets" Conference. "Forecasting Financial Markets" is an international conference on quantitative finance which is held in London in May every year. Since its inception in 1994, the conference has grown in scope and stature to become a key international meeting point for those interested in quantitative finance, with the participation of prestigious academic and research institutions from all over the world, including major central banks and quantitative fund managers. The editor has chosen to concentrate on advances in quantitative asset management and, accordingly, the papers in this book are organized around two major themes: advances in asset allocation and portfolio management, and modelling risk, return and correlation.