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

No 86, Computing in Economics and Finance 2001 from Society for Computational Economics

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.

Keywords: complexity; non-equilibrium; agents (search for similar items in EconPapers)
JEL-codes: C45 G00 G10 (search for similar items in EconPapers)
Date: 2001-04-01
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf1:86

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