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Natural Computing in Computational Finance [electronic resource] : Volume 2 / edited by Anthony Brabazon, Michael O’Neill.

By: Contributor(s): Material type: TextTextSeries: Studies in Computational Intelligence ; 185Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009Description: X, 250 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540959748
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 519 23
LOC classification:
  • TA329-348
  • TA640-643
Online resources:
Contents:
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?.
In: Springer eBooksSummary: 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.
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E-Book E-Book Central Library Available E-45584

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?.

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.

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