Natural Computing in Computational Finance

@inproceedings{Brabazon2008NaturalCI,
  title={Natural Computing in Computational Finance},
  author={Anthony Brabazon and Michael O’Neill},
  booktitle={Natural Computing in Computational Finance},
  year={2008}
}
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… 
Natural Computing in Computational Finance (Volume 2): Introduction
TLDR
The scale of NC applications in finance is illustrated by Chen & Kuo who list nearly 400 papers that had been published by 2001 on the use of evolutionary computation alone in computational economics and finance.
A Stock Market Decision Support System with a Hybrid Evolutionary Algorithm for Many-Core Graphics Processors
TLDR
This paper proposes a computational intelligence approach to stock market decision support systems based on a hybrid evolutionary algorithm with local search for many-core graphics processors, which outperforms the classic approach in terms of the financial relevance of the investment strategies discovered as well as of the computing time.
Natural Computing in Computational Finance (Volume 4): Introduction
TLDR
The inspiration for natural computing methodologies typically stem from real-world phenomena which exist in high-dimensional, dynamic, environments characteristics which fit well with the nature of financial markets.
Quantum-Inspired Evolutionary Algorithms for Financial Data Analysis
TLDR
The QIEA is described, a new computational approach which bears similarity with estimation of distribution algorithms (EDAs) which is applied to a finance problem, namely non-linear principal component analysis of implied volatilities.
Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Processors
  • Piotr Lipinski
  • Computer Science
    Natural Computing in Computational Finance
  • 2012
TLDR
This chapter concerns stock market decision support systems that build trading expertise on the basis of a set of specific trading rules, analysing financial time series of recent stock price quotations, and proposes an improvement of two popular evolutionary algorithms for rule selection by reinforcing them with two local search operators.
Practical applications of evolutionary computation to financial engineering: robust techniques for forecasting, trading, and hedging (iba, h. and aranha, c.c.; 2012)[book review]
  • Piotr Lipinski
  • Computer Science
    IEEE Computational Intelligence Magazine
  • 2012
TLDR
This book presents a systematic framework for a few significant problems in financial engineering with some evolutionary approaches to them and should be comprehensible not only for computer scientists interested in some applications, but also for financial experts or even market traders interested in advanced methods of automated trading and new tools for market analysis.
Robust optimization of algorithmic trading systems
TLDR
This thesis concludes that Evolutionary Computation techniques such as GA and GP combined with robust optimization methods are very suitable for developing trading systems, and that the systems developed using these techniques can be used to provide significant economic profits in all market conditions.
A comparative study of the canonical genetic algorithm and a real-valued quantum-inspired evolutionary algorithm
TLDR
The results show that the QIEA obtains highly competitive results when benchmarked against the GA within static environments, while substantially outperforming both binary and real‐valued representation of the GA in terms of running time.
A Neuro-evolutionary Approach to Intraday Financial Modeling
TLDR
It is shown that it is possible to obtain extremely accurate models of the variations of the price of one stock based on the prices of the other components of the stock list, which may be used for statistical arbitrage.
Generating Directional Change Based Trading Strategies with Genetic Programming
TLDR
An alternative and novel approach is explored to use an intrinsic time scale based on Directional Changes combined with Genetic Programming to find an optimal trading strategy to forecast the future price moves of a financial market.
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