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

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

    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.

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    Bibliographic Info

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 86.

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    Date of creation: 01 Apr 2001
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    Handle: RePEc:sce:scecf1:86

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    Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
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    Keywords: complexity; non-equilibrium; agents;

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