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100301s2009 gw | s |||| 0|eng d |
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|a 9783540959748
|9 978-3-540-95974-8
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|a 10.1007/978-3-540-95974-8
|2 doi
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|a 330
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|a Natural Computing in Computational Finance
|h [electronic resource] :
|b Volume 2 /
|c edited by Anthony Brabazon, Michael O’Neill.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2009.
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|a X, 250 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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|a text file
|b PDF
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 185
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|a 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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|a 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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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Applied mathematics.
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| 650 |
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|a Engineering mathematics.
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| 650 |
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|a Economics.
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|a Management science.
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|a Economics.
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| 650 |
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|a Economics, general.
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| 650 |
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|a Appl.Mathematics/Computational Methods of Engineering.
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| 650 |
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|a Artificial Intelligence (incl. Robotics).
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| 700 |
1 |
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|a Brabazon, Anthony.
|e editor.
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| 700 |
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|a O’Neill, Michael.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9783540959731
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| 830 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 185
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-540-95974-8
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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