Natural Computing in Computational Finance / Edition 1

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More About This Textbook

Overview

Natural Computing in Computational Finance is an 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.
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Product Details

  • ISBN-13: 9783642096204
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 12/8/2010
  • Series: Studies in Computational Intelligence Series , #100
  • Edition description: Softcover reprint of hardcover 1st ed. 2008
  • Edition number: 1
  • Pages: 303
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.66 (d)

Table of Contents

Natural Computing in Computational Finance (Volume 2): Introduction.- Part I FinancialModelling.- Statistical Arbitrage with Genetic Programming.- Finding Relevant Variables in a Financial Distress Prediction Problem Using Genetic Programming and Self-organizingMaps.- Ant Colony Optimization for Option Pricing.- A Neuro-evolutionary Approach for Interest Rate Modelling.- Who’s Smart andWho’s Lucky? Inferring Trading Strategy, Learning and Adaptation in Financial Markets Through Data Mining.- Part II Agent-basedModelling.- 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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