Inferring the Composition of a Trader Population in a Financial Market

Computer Science – Computational Engineering – Finance – and Science

Scientific paper

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15 pages, 2 figures, to appear as a chapter in "Econophysics and Sociophysics of Markets and Networks", Springer-Verlag

Scientific paper

We discuss a method for predicting financial movements and finding pockets of predictability in the price-series, which is built around inferring the heterogeneity of trading strategies in a multi-agent trader population. This work explores extensions to our previous framework (arXiv:physics/0506134). Here we allow for more intelligent agents possessing a richer strategy set, and we no longer constrain the estimate for the heterogeneity of the agents to a probability space. We also introduce a scheme which allows the incorporation of models with a wide variety of agent types, and discuss a mechanism for the removal of bias from relevant parameters.

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