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 [2–5]. 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?
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References
Challet D, Marsili M, Zhang YC (2005) Minority Games, Oxford University Press, Oxford.
Jefferies P, Johnson NF (2002) Designing agent-based market models; e-print cond-mat/0207523 at xxx.lanl.gov
Johnson NF, Jefferies P, Hui PM (2003) Financial Market Complexity, Oxford University Press, Oxford.
Johnson NF, Lamper D, Jefferies P, Hart ML, Howison SD (2001) Application of multi-agent games to the prediction of financial time-series, Physica A 299:222–227
Andersen JV, Sornette D (2005) A mechanism for pockets of predictability in complex adaptive systems, Europhysics Letters 70:697–703
Gupta N, Hauser R, Johnson NF (2005) Using artificial market models to forecast financial time-series. In: Workshop on Economic Heterogeneous Interacting Agents 2005, e-print physics/0506134 at http://www.xxx.lanl.gov.
Bar-Shalom Y, Li XR, Kirubarajan T (2001) Estimation with Applications to Tracking and Navigation. John Wiley and Sons, Inc.
Chiang YT, Wang LS, Chang FR, Peng HM (2002) Constrained filtering method for attitude determination using gps and gyro, IEE Proceedings-Radar, Sonar, and Navigation, 149:258–264
Wang LS, Chiang YT, Chang FR (2002) Filtering method for nonlinear systems with constraints, IEE Proceedings-Control Theory and Applications 149:525–531
Simon D, Simon DL (2003) Kalman filtering with inequality constraints for turbofan engine health estimation, Technical Report A491414, National Aeronautics and Space Administration, John H. Glenn Research Center at Lewis Field
Maybeck PS (1982) Stochastic Models, Estimation and Control. Volume 2. Academic Press, Inc.
Friedland B (1969) Treatment of bias in recursive filtering, IEEE Transactions on Automatic Control 14:359–367
Casella G, Berger RL (2002) Statistical Inference, Thomson Learning
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© 2007 Springer-Verlag Italia
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Gupta, N., Hauser, R., Johnson, N.F. (2007). Inferring the Composition of a Trader Population in a Financial Market. In: Chatterjee, A., Chakrabarti, B.K. (eds) Econophysics of Markets and Business Networks. New Economic Windows. Springer, Milano. https://doi.org/10.1007/978-88-470-0665-2_7
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DOI: https://doi.org/10.1007/978-88-470-0665-2_7
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