Natural Computing in Computational Finance, Volume 4

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Anthony Brabazon, Michael O'Neill, Dietmar Maringer
Springer Science & Business Media, Sep 10, 2011 - Computers - 202 pages
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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 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.

 

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Contents

Introduction
1
Calibrating Option Pricing Models with Heuristics
9
A Comparison between NatureInspired and Machine Learning Approaches to Detecting Trend Reversals in Financial Time Series
38
A Soft Computing Approach to Enhanced Indexation
61
Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on ManyCore Graphics Processors
78
RegimeSwitching Recurrent Reinforcement Learning in Automated Trading
93
An Evolutionary Algorithmic Investigation of US Corporate Payout Policy Determination
123
Tackling Overfitting in EvolutionaryDriven Financial Model Induction
140
An OrderDriven AgentBased Artificial Stock Market to Analyze Liquidity Costs of Market Orders in the Taiwan Stock Market
163
A SelfOrganizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment
180
Author Index
198
Subject Index
199
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