Buch, Englisch, Band 380, 202 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 527 g
Volume 4
Buch, Englisch, Band 380, 202 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 527 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-642-23335-7
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.
