On the other hand, the fundamental agents in our model “choose” a fundamental price, and change it according to the influx of news as well as the distance to the positions of the rest of the agents in the market. Heterogeneity among technical agents is achieved by assigning different parameters (“personalities”) to different subsets of the technical agent population. These traders also incur in profit taking if the price of the asset exceeds a certain threshold. Technical agents in our model follow a “Moving average oscilator” strategy, which is commonly used by real technical traders.
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Specifically, in our model, we consider two types of agent: technical and fundamental. In particular, we do not dwell on whether these rules of thumb have solid microeconomical foundations. Thus, while models with “intelligent” agents employing different strategies in realistic market environments have been proposed before, our model is motivated by the behavior of market participants following the rules of thumb employed by real life traders, while keeping the model as simple as possible. latter type of models usually have prices adjusted in a stochastic manner. The first type of models usually make use of market trading structures similar to those used in real markets, such as double auction order books, and as a consequence, the price formation is directly driven by the offers (to buy and sell) supplied by the agents. These models have been constructed, in general, in one of two ways: models in which the agents do not use a particular set of strategies, but rather participate in the market in a random fashion, and models in which the agents follow different specific strategies inspired in actual strategies used by participants of real markets, as we do in this work.
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Several Agent Based Models have been created that are capable of reproducing stylized facts and provide possible microscopic explanations of their origins. That is, to consider financial markets as something closer to what they actually are: systems where great number of different components interact amongst each other in a way that gives rise spontaneously to the observed macroscopic statistical properties.Īmong the models which approach financial markets as complex systems, there is a particular kind called “Agent Based Models” which employ a bottom-up approach and allow the modeler to trace back the emergence of the macroscopic statistical properties of the system as a consequence of the microscopic behavioral traits of its constituent agents. This situation has given rise to the exploration of financial systems as “complex systems”. To avoid creating deterministic dynamics without periods of depression or growth, DSGE models use exogenous stochastic terms which are supposed to mimic the varying conditions of the market, such as sudden peaks in the demand of a certain financial instrument or changes in the pricing of a commodity.ĭespite of the fact that these models are capable of providing some explanations of the phenomena observed in financial markets, the premises over which they are built are crude approximations of reality and as a such they are not always useful to gain insight into statistical phenomena as rich at that observed in financial time series.
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The second kind of models assume a “representative agent” for each of the participant sectors in the financial system, each of these agents attempting to their utility. The first kind of models are able to produce reasonable representations and volatility forecasts of financial systems as long as the statistical properties of the prices with which they were calibrated do not change by a large margin. The majority of approaches used today to model financial markets fall into one of two categories: statistical models adjusted to fit the history of past prices and Dynamic Stochastic General Equilibrium (DSGE) models. In this work we present and study a model of a financial market and its participants which reproduces these stylized facts.
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Yet, these investigations show that the variations in prices indeed share non trivial statistical properties, generically called stylized facts. The universality of these properties is of interest because the size, the participants and the events that affect the changes of price (returns) in a certain market may differ enormously from those that affect another. From the study of these time series, a set of statistical properties common to many different markets, time periods and instruments, have been identified.
Redtail crm book to value ratio series#
In the past five decades a great number of time series of prices of various financial markets have become available and have been subjected to analysis to characterize their statistical properties.