Erratum to: Heterogeneous expectations leading to bubbles and crashes in asset markets: Tipping point, herding behavior and group effect in an agent-based model (original) (raw)
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In this paper we present an interacting-agent model of speculative activity explaining bubbles and crashes in stock markets. We describe stock markets through an infinite-range Ising model to formulate the tendency of traders getting influenced by the investment attitude of other traders. Bubbles and crashes are understood and described qualitatively and quantitatively in terms of the classical phase transitions. When the interactions among traders become stronger and reach some critical values, a second-order phase transition and critical behaviour can be observed, and a bull market phase and a bear market phase appear. When the system stays at the bull market phase, speculative bubbles occur in the stock market. For a certain range of the external field that we shall call the investment environment, multistability and hysteresis phenomena are observed. When the investment environment reaches some critical values, the rapid changes in the distribution of investment attitude are caused. The first-order phase transition from a bull market phase to a bear market phase is considered as a stock market crash.
Herding interactions as an opportunity to prevent extreme events in financial markets
The European Physical Journal B, 2015
A characteristic feature of complex systems in general is a tight coupling between their constituent parts. In complex socioeconomic systems this kind of behavior leads to self-organization, which may be both desirable (e.g. social cooperation) and undesirable (e.g. mass panic, financial "bubbles" or "crashes"). Abundance of the empirical data as well as general insights into the trading behavior enables the creation of simple agentbased models reproducing sophisticated statistical features of the financial markets. In this contribution we consider a possibility to prevent self-organized extreme events in financial market modeling its behavior using agent-based herding model, which reproduces main stylized facts of the financial markets. We show that introduction of agents with predefined fundamentalist trading behavior helps to significantly reduce the probability of the extreme price fluctuations events. We also investigate random trading, which was previously found to be promising extreme event prevention strategy, and find that its impact on the market has to be considered among other opportunities to stabilize the markets.
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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.
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In this paper we investigate the effects of herding on asset price dynamics during continuous trading. We focus on the role of interaction among traders, and we investigate the dynamics emerging when we allow for a tendency to mimic the actions of other investors, that is, to engage in herd behavior. The model, built as a mean field in a binary setting (buy/sell decisions of a risky asset), is expressed by a three-dimensional discrete dynamical system describing the evolution of the asset price, its expected price, and its excess demand. We show that such dynamical system can be reduced to a unidirectionally coupled system. In line with the rational herd behavior literature , Herd Behavior in Financial Markets: A Review. Working paper, IMF, WP/00/48], situations of multistability are observed, characterized by strong path dependence; that is, the dynamics of the system are strongly influenced by historical accidents. We describe the different kinds of dynamic behavior observed, and we characterize the bifurcations that mark the transitions between qualitatively different time evolutions. Some situations give rise to high sensitivity with respect to small changes of the parameters and/or initial conditions, including the possibility of invest or reject cascades (i.e., sudden uncontrolled increases or crashes of the prices).
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A characteristic feature of complex systems in general is a tight coupling between their constituent parts. In complex socio-economic systems this kind of behavior leads to self-organization, which may be both desirable (e.g. social cooperation) and undesirable (e.g. mass panic, financial "bubbles" or "crashes"). Abundance of the empirical data as well as general insights into the trading behavior enables the creation of simple agent-based models reproducing sophisticated statistical features of the financial markets. In this contribution we consider a possibility to prevent self-organized extreme events in artificial financial market setup built upon a simple agent-based herding model. We show that introduction of agents with predefined fundamentalist trading behavior helps to significantly reduce the probability of the extreme price fluctuations events. We also test random trading control strategy, which was previously found to be promising, and find that its i...
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