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Profit opportunities, crash prediction and risk minimization in artificial and real-world markets


  • Neil F. Johnson, David Lamper, Paul Jefferies, Michael Hart and Sam Howison


This paper reports on the use of multi-agent games to model financial markets. Our research employs multi-agent games to address three questions which are of great practical importance in quantitative finance: how profit opportunities may be identified, large price movements predicted, and inherent risk exposure minimized. The present paper focuses on the aspect of prediction. In particular, we report a technique for predicting future movements of financial time-series using multi-agent games. A third-party game is trained on a black-box time-series, and is then run into the future to extract next-step and multi-step predictions. Such predictions have potential use not only for speculative gain, but also as the basis for improved risk management and portfolio optimization strategies.

Suggested Citation

  • Neil F. Johnson, David Lamper, Paul Jefferies, Michael Hart and Sam Howison, 2001. "Profit opportunities, crash prediction and risk minimization in artificial and real-world markets," Computing in Economics and Finance 2001 86, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:86

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    More about this item


    complexity; non-equilibrium; agents;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G00 - Financial Economics - - General - - - General


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