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Inferring the Composition of a Trader Population in a Financial Market

  • Nachi Gupta
  • Raphael Hauser
  • Neil F. Johnson
Part of the New Economic Windows book series (NEW)

Abstract

There has been an explosion in the number of models proposed for understanding and interpreting the dynamics of financial markets. Broadly speaking, all such models can be classified into two categories: (a) models which characterize the macroscopic dynamics of financial prices using time-series methods, and (b) models which mimic the microscopic behavior of the trader population in order to capture the general macroscopic behavior of prices. Recently, many econophysicists have trended towards the latter by using multi-agent models of trader populations. One particularly popular example is the so-called Minority Game [1], a conceptually simple multi-player game which can show non-trivial behavior reminiscent of real markets. Subsequent work has shown that - at least in principle - it is possible to train such multi-agent games on real market data in order to make useful predictions [2, 3, 4, 5]. However, anyone attempting to model a financial market using such multi-agent trader games, with the objective of then using the model to make predictions of real financial time-series, faces two problems: (a) How to choose an appropriate multi-agent model? (b) How to infer the level of heterogeneity within the associated multi-agent population?

Keywords

Kalman Filter Equality Constraint Inequality Constraint State Prediction Winning Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Italia 2007

Authors and Affiliations

  • Nachi Gupta
    • 1
  • Raphael Hauser
    • 1
  • Neil F. Johnson
    • 2
  1. 1.Numerical Analysis Group, Wolfson BuildingOxford University Computing LaboratoryOxfordUK
  2. 2.Department of Physics, Clarendon BuildingOxford UniversityOxfordUK

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