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03698nam a22005175i 4500 |
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978-3-642-23336-4 |
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20151204164937.0 |
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|a 9783642233364
|9 978-3-642-23336-4
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|a 10.1007/978-3-642-23336-4
|2 doi
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|a Natural Computing in Computational Finance
|h [electronic resource] :
|b Volume 4 /
|c edited by Anthony Brabazon, Michael O’Neill, Dietmar Maringer.
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| 264 |
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 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 text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 380
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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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|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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| 650 |
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0 |
|a Engineering.
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| 650 |
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|a Information technology.
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| 650 |
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|a Business
|x Data processing.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computational intelligence.
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| 650 |
1 |
4 |
|a Engineering.
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| 650 |
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|a Computational Intelligence.
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| 650 |
2 |
4 |
|a Artificial Intelligence (incl. Robotics).
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| 650 |
2 |
4 |
|a IT in Business.
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| 700 |
1 |
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|a Brabazon, Anthony.
|e editor.
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| 700 |
1 |
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|a O’Neill, Michael.
|e editor.
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| 700 |
1 |
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|a Maringer, Dietmar.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783642233357
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| 830 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 380
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-3-642-23336-4
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
|
