Natural Computing in Computational Finance
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 firs...
Shranjeno v:
| Glavni avtor: | |
|---|---|
| Drugi avtorji: | , |
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Izdaja: | X. |
| Serija: | Studies in Computational Intelligence
100 |
| Online dostop: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Sporočilo: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Natural computing in computational finance, Anthony Brabazon, Michael O'Neill (eds.), Berlin, Springer, 2008, 1 vol. (X-303 p.), Studies in computational intelligence, 978-3-540-77476-1 • Natural Computing in Computational Finance, Texte imprimé, 9783642096204 • Natural Computing in Computational Finance, Texte imprimé, 9783540848806 • Natural computing in computational finance, Anthony Brabazon, Michael O'Neill (eds.), Berlin, Springer, 2008, 1 vol. (X-303 p.), Studies in computational intelligence, 978-3-540-77476-1 |
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| 100 | 1 | |a Brabazon, Anthony. | |
| 245 | 1 | 0 | |a Natural Computing in Computational Finance |c edited by Anthony Brabazon, Michael O Neill. |
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| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 100 |x 1860-9503 | |
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| 505 | 1 | |a 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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 | ||
| 700 | 1 | |a O'Neill, Michael. |4 edt | |
| 700 | 1 | |a O'Neill, Michael, |d 19..- |4 pbd | |
| 776 | 0 | |0 135657156 |t Natural computing in computational finance |f Anthony Brabazon, Michael O'Neill (eds.) |c Berlin |n Springer |d 2008 |p 1 vol. (X-303 p.) |s Studies in computational intelligence |z 978-3-540-77476-1 | |
| 776 | 0 | |t Natural Computing in Computational Finance |b Texte imprimé |z 9783642096204 | |
| 776 | 0 | |t Natural Computing in Computational Finance |b Texte imprimé |z 9783540848806 | |
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