As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

Autorentext

Dr Christian L. Dunis is a Founding Partner of Acanto Research, where he is responsible for global risk and new products. He is also Emeritus Professor of Banking and Finance at Liverpool John Moores University where he directed the Centre for International Banking, Economics and Finance (CIBEF) from February 1999 through to August 2011. Christian holds a MSc and a Superior Studies Diploma in International Economics, and a PhD in Economics from the University of Paris.

Dr Peter W. Middleton completed his PhD at the University of Liverpool. His working experience is in Asset Management and he has published numerous articles on Financial Forecasting of Commodity spreads and Equity time series.

Dr Andreas Karathanasopoulos studied for his MSc and Phd at Liverpool John Moores University under the supervision of Professor Christian Dunis. His working experience is academic having taught at Ulster University, London Metropolitan University and the University of East London. He is currently Associate Professor at the American University of Beirut and has published over 30 articles and one book in the area of artificial intelligence.

Dr Konstantinos Theofilatos completed his MSc and PhD in the University of Patras Greece. His research interests include computational intelligence, financial time series forecasting and trading, bioinformatics, data mining and web technologies. He has published 27 publications in scientific peer reviewed journals and over 30 articles in conference proceedings.

Inhalt

1. A Review of Applications of Artificial Intelligence in Financial Domain.- SECTION I: Financial Forecasting and Trading.- 2. Trading the FTSE100 Index 'Adaptive' Modelling and Optimisation Techniques.- 3. Modelling, Forecasting and Trading the Crack A Sliding Window Approach to Training Neural Networks.- 4. GEPTrader: A new Standalone Tool for Constructing Trading Strategies with Gene Expression Programming.- SECTION II: ECONOMICS.- 5. Business Intelligence for Decision Making in Economics.- 6. An automated literature analysis on data mining applications to credit risk assessment.- SECTION III: CREDIT RISK ANALYSIS.- 7. Intelligent credit risk decision support: architecture and implementations.- 8. Artificial Intelligence for Islamic Sukuk Rating Predictions.- SECTION IV: PORTFOLIO MANAGEMENT, ANALYSIS AND OPTIMISATION.- 9. Portfolio selection as a multiperiod choice problem under uncertainty: an interation-based approach.- 10. Handling model risk in portfolio selection using a Multi-Objective Genetic Algorithm.- 11. Linear regression versus fuzzy linear regression does it make a difference in the evaluation of the performance of mutual fund managers?