31,577 research outputs found
Modeling Empirical Stock Market Behavior Using a Hybrid Agent-Based Dynamical Systems Model
We describe the development and calibration of a hybrid agent-based dynamical systems model of the stock market that is capable of reproducing empirical market behavior. The model consists of two types of trader agents, fundamentalists and noise traders, as well as an opinion dynamic for the latter (optimistic vs. pessimistic). The trader agents switch types stochastically over time based on simple behavioral rules. A system of ordinary differential equations is used to model the stock price as a function of the states of the trader agents. We show that the model can reproduce key stylized facts (e.g., volatility clustering and fat tails) while providing a behavioral interpretation of how the stock market itself can cause periods of high volatility and large price movements, even when the economic value of the stock grows at a constant rate
Multi-agent modeling and simulation of a stock market
The stock market represents complex systems where multiple agents interact. The complexity of the environment in the financial markets in general has encouraged the use of modeling by multi-agent platforms and particularly in the case of the stock market.In this paper, an agent-based simulation model is proposed to study the behavior of the volume of market transactions. The model is based on the case of a single asset and three types of investor agents. Each investor can be a zero intelligent trader, fundamentalist trader or traders using historical information in the decision making process. The goal of the study is to simulate the behavior of a stock market according to the different considered endogenous and exogenous variables
Why a simple herding model may generate the stylized facts of daily returns: Explanation and estimation
The paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model's goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified. --structural stochastic volatility,method of simulated moments,autocorrelation pattern,fat tails,bootstrapped p-values
Limited profit in predictable stock markets
It has been assumed that arbitrage profits are not possible in efficient
markets, because future prices are not predictable. Here we show that
predictability alone is not a sufficient measure of market efficiency. We
instead propose to measure inefficiencies of markets in terms of the maximal
profit an ideal trader can take out from a market. In a stock market model with
an evolutionary selection of agents this method reveals that the mean relative
amount of realizable profits is very limited and we find that it decays
with rising number of agents in the markets. Our results show that markets may
self-organize their collective dynamics such that it becomes very sensitive to
profit attacks which demonstrates that a high degree of market efficiency can
coexist with predictability.Comment: 4 pages, 4 figure
An interacting-agent model of financial markets from the viewpoint of nonextensive statistical mechanics
In this paper we present an interacting-agent model of stock markets. We
describe a stock market through an Ising-like model in order to formulate the
tendency of traders getting to be influenced by the other traders' investment
attitudes [1], and formulate the traders' decision-making regarding investment
as the maximum entropy principle for nonextensive entropy. We demonstrate that
the equilibrium probability distribution function of the traders' investment
attitude is the {\it q-exponential distribution}. We also show that the
power-law distribution of the volatility of price fluctuations, which is often
demonstrated in empirical studies, can be explained naturally by our model
which is based on the collective crowd behavior of many interacting agents.Comment: 7 pages, forthcoming into Physica A (2006
The dynamics of the leverage cycle
We present a simple agent-based model of a financial system composed of
leveraged investors such as banks that invest in stocks and manage their risk
using a Value-at-Risk constraint, based on historical observations of asset
prices. The Value-at-Risk constraint implies that when perceived risk is low,
leverage is high and vice versa, a phenomenon that has been dubbed pro-cyclical
leverage. We show that this leads to endogenous irregular oscillations, in
which gradual increases in stock prices and leverage are followed by drastic
market collapses, i.e. a leverage cycle. This phenomenon is studied using
simplified models that give a deeper understanding of the dynamics and the
nature of the feedback loops and instabilities underlying the leverage cycle.
We introduce a flexible leverage regulation policy in which it is possible to
continuously tune from pro-cyclical to countercyclical leverage. When the
policy is sufficiently countercyclical and bank risk is sufficiently low the
endogenous oscillation disappears and prices go to a fixed point. While there
is always a leverage ceiling above which the dynamics are unstable,
countercyclical leverage can be used to raise the ceiling. We also study the
impact on leverage cycles of direct, temporal control of the bank's riskiness
via the bank's required Value-at-Risk quantile. Under such a rule the regulator
relaxes the Value-at-Risk quantile following a negative stock price shock and
tightens it following a positive shock. While such a policy rule can reduce the
amplitude of leverage cycles, its effectiveness is highly dependent on the
choice of parameters. Finally, we investigate fixed limits on leverage and show
how they can control the leverage cycle.Comment: 35 pages, 9 figure
The value of information in a multi-agent market model
We present an experimental and simulated model of a multi-agent stock market
driven by a double auction order matching mechanism. Studying the effect of
cumulative information on the performance of traders, we find a non monotonic
relationship of net returns of traders as a function of information levels,
both in the experiments and in the simulations. Particularly, averagely
informed traders perform worse than the non informed and only traders with high
levels of information (insiders) are able to beat the market. The simulations
and the experiments reproduce many stylized facts of stock markets, such as
fast decay of autocorrelation of returns, volatility clustering and fat-tailed
distribution of returns. These results have an important message for everyday
life. They can give a possible explanation why, on average, professional fund
managers perform worse than the market index.Comment: 11 pages, 5 figures, published in EPJ
Agent-based simulation of a financial market
This paper introduces an agent-based artificial financial market in which
heterogeneous agents trade one single asset through a realistic trading
mechanism for price formation. Agents are initially endowed with a finite
amount of cash and a given finite portfolio of assets. There is no
money-creation process; the total available cash is conserved in time. In each
period, agents make random buy and sell decisions that are constrained by
available resources, subject to clustering, and dependent on the volatility of
previous periods. The model herein proposed is able to reproduce the
leptokurtic shape of the probability density of log price returns and the
clustering of volatility. Implemented using extreme programming and
object-oriented technology, the simulator is a flexible computational
experimental facility that can find applications in both academic and
industrial research projects.Comment: 11 pages, 3 EPS figures, LaTEX. To be published in Physica A
(Proceedings of the NATO Advanced Research Workshop on Application of Physics
in Economic Modelling, Prague 8-10 February 2001
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