  This paper outlines an agent-based model of a simple financial market in
which a single asset is available for trade by three different types of
traders. The model was first introduced in the PhD thesis of one of the
authors, see reference [1]. The simulated log returns are examined for the
presence of the stylised facts of financial data. The features of
leptokurtosis, volatility clustering and aggregational Gaussianity are
especially highlighted and studied in detail. The following ingredients are
found to be essential for the production of these stylised facts: the memory of
noise traders who make random trade decisions; the inclusion of technical
traders that trade in line with trends in the price and the inclusion of
fundamental traders who know the "fundamental value" of the stock and trade
accordingly. When these three basic types of traders are included log returns
are produced with a leptokurtic distribution and volatility clustering as well
as some further statistical features of empirical data. This enhances and
broadens our understanding of the fundamental processes involved in the
production of empirical data by the market.
