Natural Computing in Computational Finance Volume 4 1st Edition by Anthony Brabazon, ‎Michael O Neill, ‎Dietmar Maringer ISBN 3642233368 9783642233364

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ISBN 10: 3642233368 
ISBN 13: 9783642233364
Author: Anthony Brabazon, ‎Michael O Neill, ‎Dietmar Maringer

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.  

Natural Computing in Computational Finance Volume 4 1st Table of contents:

Natural Computing in Computational Finance (Volume 4): Introduction
Introduction
The Chapters
Optimisation
Model Induction
Agent-Based Modelling
Conclusions
References
Calibrating Option Pricing Models with Heuristics
Introduction
Pricing with the Characteristic Function
Black–Scholes–Merton
Merton’s Jump–Diffusion Model
The Heston Model
The Bates Model
Integration Schemes
Calibrating Model Parameters
Differential Evolution
Particle Swarm Optimisation
A Simple Hybrid
Constraints
Experiments and Results
Goodness-of-Fit
Parameters
Conditioning of the Problem
Conclusion
References
A Comparison between Nature-Inspired and Machine Learning Approaches to Detecting Trend Reversals in
Introduction
Problem Description
Anomaly Detection Methods
Particle Swarm Optimization
Negative Selection
Machine Learning Approaches
Feature Selection
Exhaustive Search
Greedy Algorithm
Experiments and Results
PSO and NS
Machine Learning
Using Feature Selection
Discussion
Conclusion
References
A Soft Computing Approach to Enhanced Indexation
Introduction
Enhanced Indexation with Cardinality Constraints
Soft-Computing Optimisation Algorithms
Fuzzy Portfolio Management
Experiments
Sample Data and Experimental Design
Computational Results
Financial Implications
Discussion – Further Research
References
Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Proce
Introduction
Problem Definition
Simple Genetic Algorithm for Rule Selection
Population-Based Incremental Learning for Rule Selection
Hybrid Evolutionary Algorithms with Local Search and Simulated Annealing
Parallel Approach to Rule Selection
Parallel Architecture and Data Structures
Parallel Population Evaluation
Parallel Local Search
Experiments
Conclusions
References
Regime-Switching Recurrent Reinforcement Learning in Automated Trading
Introduction
Literature Review
Model Description
Recurrent Reinforcement Learning
Regime-Switching Recurrent Reinforcement Learning
Differential Sharpe Ratio for Online Learning
Transition Variables
General Considerations
Volatility
Volume
Rate of Information Arrival
Experiments
Setup
Artificial Data
Real Financial Datasets
Analysis
Further Experiments
Conclusion
A Estimation of Transition Variables
References
An Evolutionary Algorithmic Investigation of US Corporate Payout Policy Determination
Introduction
Literature Review
Sample Data and Proxy Explanatory Valriables
Methodology
Genetic Programming
Evolving Symbolic Regression Programs
Model Overfitting Avoidance
Experimental Context
Results
Summary and Concluding Remarks
References
Tackling Overfitting in Evolutionary-Driven Financial Model Induction
Introduction
Causes of Overfitting
Structure of Chapter
Background
Previous Attempts to Tackle Overfitting in Genetic Programming
Early Stopping
Grammatical Evolution: A Brief Introduction
Some Experiments to Illustrate Overfitting in Symbolic Regression Problems
Setup
Results
Solutions to Prevent Overfitting – Stopping Criteria
Investigations: Stopping Criteria applied to a Financial Dataset
Grammar-based GP Experimental Setup
Financial Time-Series and Trading Methodology
Results and Discussion
Conclusions
References
An Order-Driven Agent-Based Artificial Stock Market to Analyze Liquidity Costs of Market Orders in t
Introduction
Background and RelatedWork
Order-Driven Double-AuctionMarkets
The DFGIS Model
The Guo Agent-Based Stock Market Model
An Agent-Based Taiwan Stock Market Model
Buy and Sell Orders
Event-Time Model
Order Pricing Rules
Liquidity Costs
Measurement ofModel Parameters
Program Implementation and System Flow
The Data Set
Experimental Setup
Simulation Results and Analysis
Discussions
Concluding Remarks
References
Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evol
Introduction
Agent-Based Financial Models
N-Type Designs
Autonomous Agent Designs
Limitations of the Agent-Based Financial Models
Model
Genetic Programming (GP)
Self OrganizingMaps (SOM)
Experimental Designs
Testing Methodology

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