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Natural Computing in Computational Finance

Overview of attention for book
Cover of 'Natural Computing in Computational Finance'

Table of Contents

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    Book Overview
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    Chapter 1 Natural Computing in Computational Finance (Volume 4): Introduction
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    Chapter 2 Calibrating Option Pricing Models with Heuristics
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    Chapter 3 A Comparison between Nature-Inspired and Machine Learning Approaches to Detecting Trend Reversals in Financial Time Series
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    Chapter 4 A Soft Computing Approach to Enhanced Indexation
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    Chapter 5 Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Processors
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    Chapter 6 Regime-Switching Recurrent Reinforcement Learning in Automated Trading
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    Chapter 7 An Evolutionary Algorithmic Investigation of US Corporate Payout Policy Determination
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    Chapter 8 Tackling Overfitting in Evolutionary-Driven Financial Model Induction
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    Chapter 9 An Order-Driven Agent-Based Artificial Stock Market to Analyze Liquidity Costs of Market Orders in the Taiwan Stock Market
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    Chapter 10 Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment
Attention for Chapter 9: An Order-Driven Agent-Based Artificial Stock Market to Analyze Liquidity Costs of Market Orders in the Taiwan Stock Market
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Citations

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Chapter title
An Order-Driven Agent-Based Artificial Stock Market to Analyze Liquidity Costs of Market Orders in the Taiwan Stock Market
Chapter number 9
Book title
Natural Computing in Computational Finance
Published by
Springer Berlin Heidelberg, January 2012
DOI 10.1007/978-3-642-23336-4_9
Book ISBNs
978-3-64-223335-7, 978-3-64-223336-4
Authors

Yi-Ping Huang, Shu-Heng Chen, Min-Chin Hung, Tina Yu

Editors

Anthony Brabazon, Michael O’Neill, Dietmar Maringer

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 100%
Readers by discipline Count As %
Unknown 1 100%

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