63,047 research outputs found
Social Simulation of Stock Markets: Taking It to the Next Level
This paper studies the use of social simulation in linking micro level investor behaviour and macro level stock market dynamics. Empirical data from a survey on individual investors\' decision-making and social interaction was used to formalize the trading and interaction rules of the agents of the artificial stock market SimStockExchange. Multiple simulation runs were performed with this artificial stock market, which generated macro level results, like stock market prices and returns over time. These outcomes were subsequently compared to empirical macro level data from real stock markets. Partial qualitative as well as quantitative agreement between the simulated asset returns distributions and the asset returns distributions of the real stock markets was found.Agent-Based Computational Finance, Artificial Stock Markets, Behavioral Finance, Micro-Macro Links, Multi-Agent Simulation, Stock Market Characteristics
Stochastic Opinion Formation in Scale-Free Networks
The dynamics of opinion formation in large groups of people is a complex
non-linear phenomenon whose investigation is just at the beginning. Both
collective behaviour and personal view play an important role in this
mechanism. In the present work we mimic the dynamics of opinion formation of a
group of agents, represented by two state , as a stochastic response of
each of them to the opinion of his/her neighbours in the social network and to
feedback from the average opinion of the whole. In the light of recent studies,
a scale-free Barab\'asi-Albert network has been selected to simulate the
topology of the interactions. A turbulent-like dynamics, characterized by an
intermittent behaviour, is observed for a certain range of the model
parameters. The problem of uncertainty in decision taking is also addressed
both from a topological point of view, using random and targeted removal of
agents from the network, and by implementing a three state model, where the
third state, zero, is related to the information available to each agent.
Finally, the results of the model are tested against the best known network of
social interactions: the stock market. A time series of daily closures of the
Dow Jones index has been used as an indicator of the possible applicability of
our model in the financial context. Good qualitative agreement is found.Comment: 24 pages and 13 figures, Physical Review E, in pres
Crashes as Critical Points
We study a rational expectation model of bubbles and crashes. The model has
two components : (1) our key assumption is that a crash may be caused by local
self-reinforcing imitation between noise traders. If the tendency for noise
traders to imitate their nearest neighbors increases up to a certain point
called the ``critical'' point, all noise traders may place the same order
(sell) at the same time, thus causing a crash. The interplay between the
progressive strengthening of imitation and the ubiquity of noise is
characterized by the hazard rate, i.e. the probability per unit time that the
crash will happen in the next instant if it has not happened yet. (2) Since the
crash is not a certain deterministic outcome of the bubble, it remains rational
for traders to remain invested provided they are compensated by a higher rate
of growth of the bubble for taking the risk of a crash. Our model distinguishes
between the end of the bubble and the time of the crash,: the rational
expectation constraint has the specific implication that the date of the crash
must be random. The theoretical death of the bubble is not the time of the
crash because the crash could happen at any time before, even though this is
not very likely. The death of the bubble is the most probable time for the
crash. There also exists a finite probability of attaining the end of the
bubble without crash. Our model has specific predictions about the presence of
certain critical log-periodic patterns in pre-crash prices, associated with the
deterministic components of the bubble mechanism. We provide empirical evidence
showing that these patterns were indeed present before the crashes of 1929,
1962 and 1987 on Wall Street and the 1997 crash on the Hong Kong Stock
Exchange. These results are compared with statistical tests on synthetic data.Comment: A total of 40 pages including 9 figures and 6 table
Wealth distribution across communities of adaptive financial agents
This paper studies the trading volumes and wealth distribution of a novel
agent-based model of an artificial financial market. In this model,
heterogeneous agents, behaving according to the Von Neumann and Morgenstern
utility theory, may mutually interact. A Tobin-like tax (TT) on successful
investments and a flat tax are compared to assess the effects on the agents'
wealth distribution. We carry out extensive numerical simulations in two
alternative scenarios: i) a reference scenario, where the agents keep their
utility function fixed, and ii) a focal scenario, where the agents are adaptive
and self-organize in communities, emulating their neighbours by updating their
own utility function. Specifically, the interactions among the agents are
modelled through a directed scale-free network to account for the presence of
community leaders, and the herding-like effect is tested against the reference
scenario. We observe that our model is capable of replicating the benefits and
drawbacks of the two taxation systems and that the interactions among the
agents strongly affect the wealth distribution across the communities.
Remarkably, the communities benefit from the presence of leaders with
successful trading strategies, and are more likely to increase their average
wealth. Moreover, this emulation mechanism mitigates the decrease in trading
volumes, which is a typical drawback of TTs.Comment: 18 pages, 7 figures, published in New Journal of Physic
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