Natural Computing in Computational Finance
Volume 4
(Sprache: Englisch)
Like its three predecessors, this fourth volume in its series covers cutting-edge natural computing and agent-based methodologies in computational finance and economics: option model calibration, financial trend reversal detection, algorithmic trading and more.
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Produktinformationen zu „Natural Computing in Computational Finance “
Like its three predecessors, this fourth volume in its series covers cutting-edge natural computing and agent-based methodologies in computational finance and economics: option model calibration, financial trend reversal detection, algorithmic trading and more.
Klappentext zu „Natural Computing in Computational Finance “
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.
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.
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
... mehr
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.
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.
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.
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.
... weniger
Inhaltsverzeichnis zu „Natural Computing in Computational Finance “
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.
Bibliographische Angaben
- 2016, Softcover reprint of the original 1st ed. 2012, X, 202 Seiten, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Anthony Brabazon, Michael O'Neill, Dietmar Maringer
- Verlag: Springer, Berlin
- ISBN-10: 3662519984
- ISBN-13: 9783662519981
Sprache:
Englisch
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