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|a 9783642233364
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|a Brabazon, Anthony
|e [editor]
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|a Natural Computing in Computational Finance
|h Elektronische Ressource
|b Volume 4
|c edited by Anthony Brabazon, Michael O'Neill, Dietmar Maringer
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250 |
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|a 1st ed. 2012
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260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2012, 2012
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300 |
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|a X, 202 p. 62 illus., 25 illus. in color
|b online resource
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|a 1 Natural Computing in Computational Finance (Volume 4): Introduction -- 2 Calibrating Option Pricing Models with Heuristics -- 3 A Comparison Between Nature-Inspired and Machine Learning Approaches to Detecting Trend Reversals in Financial Time Series -- 4 A soft computing approach to enhanced indexation -- 5 Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Processors -- 6 Regime-Switching Recurrent Reinforcement Learning in Automated Trading -- 7 An Evolutionary Algorithmic Investigation of US Corporate Payout Policy Determination -- 8 Tackling Overfitting in Evolutionary-driven Financial Model Induction -- 9 An Order-Driven Agent-Based Artificial Stock Market to Analyze Liquidity Costs of Market Orders in the Taiwan Stock Market -- 10 Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment
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653 |
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|a Computational intelligence
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653 |
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|a Artificial Intelligence
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653 |
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|a Computational Intelligence
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653 |
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|a IT in Business
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653 |
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|a Artificial intelligence
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653 |
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|a Business information services
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700 |
1 |
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|a O'Neill, Michael
|e [editor]
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700 |
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|a Maringer, Dietmar
|e [editor]
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041 |
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7 |
|a eng
|2 ISO 639-2
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|b Springer
|a Springer eBooks 2005-
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|a Studies in Computational Intelligence
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|a 10.1007/978-3-642-23336-4
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|u https://doi.org/10.1007/978-3-642-23336-4?nosfx=y
|x Verlag
|3 Volltext
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|a 006.3
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|a This book follows on from Natural Computing in Computational Finance Volumes I, II and III. As in the previous volumes of this series, the book consists of a series of chapters each of which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics.
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