Natural computing in computational finance
著者
書誌事項
Natural computing in computational finance
（Studies in computational intelligence, v. 100,
Springer, c2008-
- [v. 1]
- v. 2
大学図書館所蔵 全6件
注記
Includes bibliographical references and index
内容説明・目次
- 巻冊次
-
[v. 1] ISBN 9783540774761
内容説明
Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modeling in modern computational finance. Following an introductory chapter the book is organized into three sections. The first section deals with optimization applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed.
The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance.
目次
Optimisation.- Natural Computing in Computational Finance: An Introduction.- Constrained Index Tracking under Loss Aversion Using Differential Evolution.- An Evolutionary Approach to Asset Allocation in Defined Contribution Pension Schemes.- Evolutionary Strategies for Building Risk-Optimal Portfolios.- Evolutionary Stochastic Portfolio Optimization.- Non-linear Principal Component Analysis of the Implied Volatility Smile using a Quantum-inspired Evolutionary Algorithm.- Estimation of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging Optimisation Algorithm.- Model Induction.- Fuzzy-Evolutionary Modeling for Single-Position Day Trading.- Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming.- Using Kalman-filtered Radial Basis Function Networks for Index Arbitrage in the Financial Markets.- On Predictability and Profitability: Would GP Induced Trading Rules be Sensitive to the Observed Entropy of Time Series?.- Hybrid Neural Systems in Exchange Rate Prediction.- Agent-based Modelling.- Evolutionary Learning of the Optimal Pricing Strategy in an Artificial Payment Card Market.- Can Trend Followers Survive in the Long-Run% Insights from Agent-Based Modeling.- Co-Evolutionary Multi-Agent System for Portfolio Optimization.
- 巻冊次
-
v. 2 ISBN 9783540959731
内容説明
Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modelling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent-based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems.
This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer's Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007.
目次
Natural Computing in Computational Finance (Volume 2): Introduction.- Natural Computing in Computational Finance (Volume 2): Introduction.- I Financial Modelling.- Statistical Arbitrage with Genetic Programming.- Finding Relevant Variables in a Financial Distress Prediction Problem Using Genetic Programming and Self-organizing Maps.- Ant Colony Optimization for Option Pricing.- A Neuro-Evolutionary Approach for Interest Rate Modelling.- Who's Smart and Who's Lucky? Inferring Trading Strategy, Learning and Adaptation in Financial Markets through Data Mining.- II Agent-Based Modelling.- Financial Bubbles: A Learning Effect Modelling Approach.- Evolutionary Computation and Artificial Financial Markets.- Classical and Agent-Based Evolutionary Algorithms for Investment Strategies Generation.- Income Distribution and Lottery Expenditures in Taiwan: An Analysis Based on Agent-Based Simulation.- The Emergence of a Market: What Efforts Can Entrepreneurs Make?.
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