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Application of multi-agent games to the prediction of financial time-series

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


We report on a technique based on multi-agent games which has potential use in the prediction of future movements of financial time-series. 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. In addition to the possibility of identifying profit opportunities, the technique may prove useful in the development of improved risk management strategies.

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