Financial prices have been found to exhibit some universal characteristics 1±6 that resemble the scaling laws characterizing physical systems in which large numbers of units interact. This raises the question of whether scaling in ®nance emerges in a similar wayÐfrom the interactions of a large ensemble of market participants. However, such an explanation is in contradiction to the prevalent`ef®cient market hypothesis' 7 in economics, which assumes that the movements of ®nancial prices are an immediate and unbiased re¯ection of incoming news about future earning prospects. Within this hypothesis, scaling in price changes would simply re¯ect similar scaling in the`input' signals that in¯uence them. Here we describe a multi-agent model of ®nancial markets which supports the idea that scaling arises from mutual interactions of participants. Although the`news arrival process' in our model lacks both power-law scaling and any temporal dependence in volatility, we ®nd that it generates such behaviour as a result of interactions between agents.In our model, the pool of traders is divided into two groups: the ®rst group (`fundamentalists') follows the premise of the ef®cient market hypothesis in that they expect the price (p) to follow the socalled fundamental value of the asset ( p f ), which is the discounted sum of expected future earnings (for example, dividend payments). A fundamentalist trading strategy consists of buying (selling) when the actual market price is believed to be below (above) the fundamental value. The second group (called`noise traders' following established terminology in economics 8 ), however, does not believe in an immediate tendency of the price to revert to its underlying fundamental value. Instead of focusing on fundamentals, these agents attempt to identify price trends and patterns (charts), and also consider the behaviour of other traders as a source of information, which results in a tendency towards herding behaviour. Furthermore (because it is important for the resulting market operations whether a noise trader believes in a rising or declining market), we further distinguish between optimistic and pessimistic individuals in this group: optimists will buy additional units of the asset, whereas the pessimists will sell part of their actual holdings of the asset.The main building blocks of the model are movements of individuals from one group to another together with the (exogenous) changes of the fundamental value and the (endogenous) price changes resulting from the agents' market operations. A distinguishing feature of our approach as compared with other recent simulation models 9±14 is that we adopt a mass-statistical formalization inspired by statistical physics 15,16 : individuals react to certain economic forces by changing their behaviour with a certain (endogenous) probability. As a simple formalization of movements into and out of the three groups we use exponential functions, so that a switch from one group to another occurs with a certain endogenous and time-varying probabili...

No abstract

The behavioral origins of the stylized facts of financial returns have been addressed in a growing body of agent-based models of financial markets. While the traditional efficient market viewpoint explains all statistical properties of returns by similar features of the news arrival process, the more recent behavioral finance models explain them as imprints of universal patterns of interaction in these markets. In this paper we contribute to this literature by introducing a very simple agent-based model in which the ubiquitous stylized facts (fat tails, volatility clustering) are emergent properties of the interaction among traders. The simplicity of the model allows us to estimate the underlying parameters, since it is possible to derive a closed form solution for the distribution of returns. We show that the tail shape characterizing the fatness of the unconditional distribution of returns can be directly derived from some structural variables that govern the traders' interactions, namely the herding propensity and the autonomous switching tendency.

No abstract