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Natural Computing in Computational Finance Volume 4 /

This book follows on from Natural Computing in Computational Finance  Volumes I, II and III.   As in the previous volumes of this series, the  book consists of a series of  chapters each of  which was selected following a rigorous, peer-reviewed, selection process.  The chapters illustrate the appli...

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Sparad:
Bibliografiska uppgifter
Institutionell upphovsman: SpringerLink (Online service)
Övriga upphovsmän: Brabazon, Anthony. (Utgivare, redaktör, sammanställare, http://id.loc.gov/vocabulary/relators/edt), O'Neill, Michael. (Utgivare, redaktör, sammanställare, http://id.loc.gov/vocabulary/relators/edt), Maringer, Dietmar. (Utgivare, redaktör, sammanställare, http://id.loc.gov/vocabulary/relators/edt)
Materialtyp: Elektronisk E-bok
Språk:English
Publicerad: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Upplaga:1st ed. 2012.
Serie:Studies in Computational Intelligence, 380
Ämnen:
Computational intelligence.
Artificial intelligence.
Information technology.
Business—Data processing.
Computational Intelligence.
Artificial Intelligence.
IT in Business.
Länkar:https://doi.org/10.1007/978-3-642-23336-4
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Innehållsförteckning:
  • 1 Natural Computing in Computational Finance (Volume 4): Introduction
  • 2 Calibrating Option Pricing Models with Heuristics
  • 3 A Comparison Between Nature-Inspired and Machine Learning Approaches to Detecting Trend Reversals in Financial Time Series
  • 4 A soft computing approach to enhanced indexation
  • 5 Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Processors
  • 6 Regime-Switching Recurrent Reinforcement Learning in Automated Trading
  • 7 An Evolutionary Algorithmic Investigation of US Corporate Payout Policy Determination
  • 8 Tackling Overfitting in Evolutionary-driven Financial Model Induction
  • 9 An Order-Driven Agent-Based Artificial Stock Market to Analyze Liquidity Costs of Market Orders in the Taiwan Stock Market
  • 10 Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment.

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