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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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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