Financial Decision Making Using Computational Intelligence [electronic resource] /
Doumpos, Michael.editor.
Zopounidis, Constantin.editor.
Pardalos, Panos M.editor.
Financial Decision Making Using Computational Intelligence covers all the recent developments in complex financial decision making through computational intelligence approaches. Computational intelligence has evolved rapidly in recent years and it is now one of the most active fields in operations research and computer science. The increasing complexity of financial problems and the enormous volume of financial data often make it difficult to apply traditional modeling and algorithmic procedures. In this context, the field of computational intelligence provides a wide range of useful techniques, including new modeling tools for decision making under risk and uncertainty, data mining techniques for analyzing complex data bases, and powerful algorithms for complex optimization problems.   This book presents the recent advances made in financial decision making using computational intelligence, covering both new methodological developments as well as new emerging application areas. This work covers a wide range of topics related to financial decision making, financial modeling, risk management, and financial engineering, including algorithmic trading, financial time-series analysis, asset pricing, portfolio management, auction markets, and insurance services. Practitioners in the financial industry as well as operations researchers, management scientists, and data analysts will find this publication highly useful.
Description based upon print version of record.
Includes bibliographical references and index.
Preface -- List of Contributors -- 1. Statistically Principled Application of Computational Intelligence Techniques for Finance (J.V. Healy) -- 2. Can Artificial Traders Learn and Err Like Human Traders? A New Direction for Computational Intelligence in Behavioral Finance (S.-H. Chen, K.-C. Shih, C.-C. Tai) -- 3. Application of Intelligent Systems for News Analytics (C. Bozic, S. Chalup, D. Seese) -- 4. Modelling and Trading the Greek Stock Market with Hybrid ARMA-Neural Network Models (C. L. Dunis, J. Laws, A. Karathanasopoulos) -- 5. Pattern Detection and Analysis in Financial Time Series Using Suffix Arrays (K. F. Xylogiannopoulos, P. Karampelas, R. Alhajj) -- 6. Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather Derivatives (A. Agapitos, M. O’Neill, A. Brabazon) -- 7. Evolution Strategies for IPO Underpricing Prediction (D. Quintana, C. Luque, J. M. Valls, P. Isasi) -- 8. Bayesian Networks for Portfolio Analysis and Optimization (S. Villa, F. Stella) -- 9. Markov Chains in Modelling of the Russian Financial Market (G. A. Bautin and V. A. Kalyagin) -- 10. Fuzzy Portfolio Selection Models: A Numerical Study (En. Vercher and J. D. Bermúdez) -- 11. Financial Evaluation of Life Insurance Policies in High Performance Computing Environments (S. Corsaro, P. L. De Angelis, Z. Marino, P. Zanetti) -- Index.
Financial Decision Making Using Computational Intelligence covers all the recent developments in complex financial decision making through computational intelligence approaches. Computational intelligence has evolved rapidly in recent years and it is now one of the most active fields in operations research and computer science. The increasing complexity of financial problems and the enormous volume of financial data often make it difficult to apply traditional modeling and algorithmic procedures. In this context, the field of computational intelligence provides a wide range of useful techniques, including new modeling tools for decision making under risk and uncertainty, data mining techniques for analyzing complex data bases, and powerful algorithms for complex optimization problems.   This book presents the recent advances made in financial decision making using computational intelligence, covering both new methodological developments as well as new emerging application areas. This work covers a wide range of topics related to financial decision making, financial modeling, risk management, and financial engineering, including algorithmic trading, financial time-series analysis, asset pricing, portfolio management, auction markets, and insurance services. Practitioners in the financial industry as well as operations researchers, management scientists, and data analysts will find this publication highly useful.
Operations research.
Management science.
Finance.
Operations Research, Management Science.
Finance, general.