Agent-based Financial Market Model Research Articles

A simple learning agent interacting with an agent-based market model

We consider the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. Trading takes place asynchronously through a matching engine in event time. The optimal execution agent is considered at different levels of initial order sizes and differently sized state spaces. The resulting impact on the agent-based model and market is considered using a calibration approach that explores changes in the empirical stylised facts and price impact curves. Convergence, volume trajectory and action trace plots are used to visualise the learning dynamics. The smaller state space agents had the number of states they visited converge much faster than the larger state space agents, and they were able to start learning to trade intuitively using the spread and volume states. We find that the moments of the model are robust to the impact of the learning agents, except for the Hurst exponent, which was lowered by the introduction of strategic order-splitting. The introduction of the learning agent preserves the shape of the price impact curves but can reduce the trade-sign auto-correlations and increase the micro-price volatility when the trading volumes increase.

The impact of the SARS-CoV-2 pandemic on financial markets: a seismologic approach

This work investigates financial volatility cascades generated by SARS-CoV-2 related news using concepts developed in the field of seismology. We analyze the impact of socio-economic and political announcements, as well as of financial stimulus disclosures, on the reference stock markets of the United States, United Kingdom, Spain, France, Germany and Italy. We quantify market efficiency in processing SARS-CoV-2 related news by means of the observed Omori power-law exponents and we relate these empirical regularities to investors’ behavior through the lens of a stylized Agent-Based financial market model. The analysis reveals that financial markets may underreact to the announcements by taking a finite time to re-adjust prices, thus moving against the efficient market hypothesis. We observe that this empirical regularity can be related to the speculative behavior of market participants, whose willingness to switch toward better performing investment strategies, as well as their degree of reactivity to price trend or mispricing, can induce long-lasting volatility cascades.

Market entry waves and volatility outbursts in stock markets

We develop a simple agent-based financial market model in which speculators’ market entry decisions are subject to herding behavior and market risk. In addition, speculators’ orders depend on price trends, market misalignments and fundamental news. Using a mix of analytical and numerical tools, we show that a herding-induced market entry wave may amplify excess demand, triggering lasting volatility outbursts. Eventually, however, higher stock market risk reduces stock market participation and volatility decreases again. Simulations furthermore reveal that our approach is also able to produce bubbles and crashes, excess volatility, fat-tailed return distributions and serially uncorrelated price changes. Moreover, trading volume is persistent and correlated with volatility.

Self-Organization, Resilience and Robustness of Complex Systems Through an Application to Financial Market from an Agent-Based Approach

This paper introduces the implementation of a computational agent-based financial market model in which the system is described on both microscopic and macroscopic levels. This artificial financial market model is used to study the system response when a shock occurs. Indeed, when a market experiences perturbations, financial systems behavior can exhibit two different properties: resilience and robustness. Through simulations and different scenarios of market shocks, these system properties are studied. The results notably show that the emergence of collective herding behavior when market shock occurs leads to a temporary disruption of the system self-organization. Numerical simulations highlight that the market can absorb strong mono-shocks but can also be led to rupture by low but repeated perturbations.

Heterogeneity, spontaneous coordination and extreme events within large-scale and small-scale agent-based financial market models

We propose a novel agent-based financial market framework in which speculators usually follow their own individual technical and fundamental trading rules to determine their orders. However, there are also sunspot-initiated periods in which their trading behavior is correlated. We are able to convert our (very) simple large-scale agent-based model into a simple small-scale agent-based model and show that our framework is able to produce bubbles and crashes, excess volatility, fat-tailed return distributions, serially uncorrelated returns and volatility clustering. While lasting volatility outbursts occur if the mass of speculators switches to technical analysis, extreme price changes emerge if sunspots coordinate temporarily the behavior of speculators.

On the bimodality of the distribution of the S&P 500's distortion: Empirical evidence and theoretical explanations

After showing that the distribution of the S&P 500's distortion, i.e. the log difference between its real stock market index and its real fundamental value, is bimodal, we demonstrate that agent-based financial market models may explain this puzzling observation. Within these models, speculators apply technical and fundamental analysis to predict asset prices. Since destabilizing technical trading dominates the market near the fundamental value, asset prices tend to be either overvalued or undervalued. Interestingly, the bimodality of the distribution of the S&P 500's distortion confirms an implicit prediction of a number of seminal agent-based financial market models.

Piecewise-Linear Maps and Their Application to Financial Markets

The goal of this paper is to review some work on agent-based financial market models in which the dynamics is driven by piecewise-linear maps. As we will see, such models allow deep analytical insights into the functioning of financial markets, may give rise to unexpected dynamics effects, allow explaining a number of important stylized facts of financial markets, and offer novel policy recommendations. However, much remains to be done in this rather new research field. We hope that our paper attracts more scientists to this area.

Optimal monetary policy in a new Keynesian model with animal spirits and financial markets

This article presents a macro-finance-interaction model that integrates a NKM with bounded rationality and an agent-based financial market model. We derive four interactive channels between the two sectors where two channels are strictly microfounded. We analyze the impact of the different channels on economic stability and derive optimal (conventional and unconventional) monetary policy rules. We find that coefficients of optimal Taylor rules do not significantly change if financial market stabilization becomes part of the central bank׳s objective function. Additionally, we show that rule-based, backward-looking monetary policy creates huge instabilities if expectations are boundedly rational. Our model is externally validated by showing that it generates fat tailed output growth rates.

FINANCIAL TRANSACTION TAXATION IN AGENT-BASED SIMULATION

The aim of this paper is to investigate the impact of fi nancial transaction taxes (FTTs) on the stability of fi nancial markets. This paper presents an agent-based fi nancial market model and simulations in which agents follow technical and fundamental trading rules to determine their speculative investment positions. The model developed by Westerhoff (2009) was chosen for implementation and was extended by FTT and arising transaction costs. Because FTTs may be defi ned in various ways, this paper defi nes assets as tax objects. The model includes direct interactions between speculators, which may lead them to decide to change their trading behavior and addresses a technical and a fundamental strategy of market participants. The results suggest that the modifi ed model has a tendency to stabilize itself in the long term if fundamental trading rules outweigh the technical trading method. This model could be used when bubbles and crashes occur in fi nancial markets. Asset prices would be stabilized because their value targets near the fundamental value and volatility would also be minimized. Setting FTTs at a low rate for market stabilization is important. If FTTs and consequent transaction costs are too high, then the fi nancial system will destabilize and prices will grow without limit. The model described in this paper explores dependence market stability to the extent of FTTs. However, the model should not be interpreted as a model only for the introduction of FTT, but as a general model of transaction costs’ infl uence on the fi nancial market.

Speculative behavior and the dynamics of interacting stock markets

We develop a simple agent-based financial market model in which heterogeneous speculators apply technical and fundamental analysis to trade in two different stock markets. Speculators׳ strategy/market selections are repeated at each time step and depend on predisposition effects, herding behavior and market circumstances. Simulations reveal that our model is able to explain a number of nontrivial statistical properties of and between international stock markets, including bubbles and crashes, fat-tailed return distributions, volatility clustering, persistent trading volume, coevolving stock prices and cross-correlated volatilities. Against this background, our model may be deemed to have been validated.

Disposition Effect in an Agent-based Financial Market Model

Disposition effect, which refers to investors’ being reluctant to realize losses, is very common in financial markets, especially in mainland China. This paper introduces the disposition effect into a multi-agent model, to research investor behavior and its impact on financial markets. As the result of computer simulation, disposition effect reveals asymmetric volatility which reflects the actual situation in mainland China market, i.e. the impact of bad news on volatility is greater than the impact of good news of the same magnitude. Sensitivity analysis shows that investors’ disposition behaviour slows the release rate of news, makes market more stable around the fundamental price. Meanwhile, proper level of disposition effect can avoid some loss in investment, and make chartist get relative higher return than usual. These conclusions could hopefully offer insights and effective support for investment decision-making and policy regulation.

Securities Transaction Tax and Stock Market Behavior in an Agent-based Financial Market Model

As highly related to the investors’ earnings expectations and trading decision-making behavior, securities transaction tax (STT) has long been regarded as a typical regulatory mechanism exploited by policy makers. However, neither theoretical analysis nor empirical studies reach consensus about the role and policy effect of the securities transaction tax. Within the framework of agent-based computational finance, this paper presents a new artificial stock market model with heterogeneous agents, which allows us to assess the impacts of varying STTs on market behavior to come to robust conclusions. First we investigate the dynamics of benchmark market with no tax levied, and then market behaviors with different STTs are thoroughly checked. The results show that a modest transactions tax does contribute to stabilize markets by reducing market volatility, but its negative effects on market efficiency cannot be ignored at the same time. The findings suggest that regulatory authorities should introduce STT discreetly to strike a balance between stability and efficiency.

Structural stochastic volatility in asset pricing dynamics: Estimation and model contest

In the framework of small-scale agent-based financial market models, the paper starts out from the concept of structural stochastic volatility, which derives from different noise levels in the demand of fundamentalists and chartists and the time-varying market shares of the two groups. It advances several different specifications of the endogenous switching between the trading strategies and then estimates these models by the method of simulated moments (MSMs), where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. In addition to the standard version of MSM with a quadratic loss function, we also take into account how often a great number of Monte Carlo simulation runs happen to yield moments that are all contained within their empirical confidence intervals. The model contest along these lines reveals a strong role for a (tamed) herding component. The quantitative performance of the winner model is so good that it may provide a standard for future research.

Biased trader model and analysis of financial market dynamics

This study focuses on various trader behaviors that affect market dynamics. In particular, the effects of a covering mechanism, learning mechanism and bias mechanism are analyzed through agent-based financial market model. An XCS classifier system is used to model trader learning mechanism. A trader model is proposed to formulate a trader decision model that combines bias mechanisms with learning mechanisms. The results reveal that biased traders survive under evolving markets and affect price dynamics. The model contributes to understanding the market behavior and potential sources of deviation from efficient market equilibrium.

Transaction Taxes and Traders with Heterogeneous Investment Horizons in an Agent-Based Financial Market Model

This heterogeneous interacting agents model of a financial market is a generalization of the model proposed by Westerhoff (The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies) by traders who are allowed to have different investment horizons as introduced by Demary (Who Does a Currency Transaction Tax Harm More: Short-term Speculators or Long-term Investors?). Our research goals are, first, to study what consequences the introduction of heterogeneous investment horizons has for agent-based financial market models and second, how effective transaction taxes are in stabilizing financial markets. In detail, we are interested in how the popularity of different trading rules and investment horizons change due to taxation and how emergent properties from the interaction of traders like bubbles and crashes, excess volatility, excess kurtosis and volatility clustering change. Numerical simulations reveal that under taxation traders abstain from short-term trading in favour of longer investment horizons. This change in behavior leads to less excess volatility and diminishing volatility clusters for small tax rates. When the tax rate exceeds a certain threshold, excess volatility and misalignments increase as also found in Westerhoff (Heterogeneous Traders and the Tobin Tax). The reason is, that the longer term fundamentalist trading rule becomes unpopular in favor of the longer term trend-chasing rule.

Disclosure Requirements, the Release of New Information and Market Efficiency: New Insights from Agent-Based Models

We explore how disclosure requirements that regulate the release of new information may affect the dynamics of financial markets. Our analysis is based on three agent-based financial market models that are able to produce realistic financial market dynamics. We discover that the average deviation between market prices and fundamental values increases if new information is released with a delay, while the average price volatility is virtually unaffected by such regulations. Interestingly, the tails of the distribution of returns become fatter if fundamental data is released less continuously, indicating an increase in financial market risk. --

Transaction taxes, greed and risk aversion in an agent-based financial market model

Recent agent-based financial market models came to the result that taxing financial transactions does not per se increase financial stability and that the response of volatility and misalignments to rising tax rates seem to be u-shaped. Moreover, greed and the risk appetite of traders are often blamed for financial instability and there is no evidence how greed and risk aversion affect the effectiveness of regulations in financial markets. We aim to add to this gap in the literature by analyzing how the effectiveness of transaction taxes depend on different behavioral patterns within an agent-based framework. Our simulations indicate that a tax rate of 0.1% demarcates the stabilizing tax regime from the destabilizing one. We figure out that transaction taxes are less effective, either when chartists trade more aggressively, fundamentalists trade less aggressively, agents switch more frequently between trading strategies or only have short memory in their fitness measures. Lower risk aversion of agents, however, makes higher tax rates more effective as indicated by a flatter volatility response curve. We conclude that additional regulations should concentrate on the traders’ responsibilities for their risk-exposure.

Estimation of a Structural Stochastic Volatility Model of Asset Pricing

The paper estimates an elementary agent-based financial market model recently put forward by the same authors. Invoking the two trader types of fundamentalists and chartists, it comprises four features: price determination by excess demand; a herding mechanism that gives rise to a macroscopic adjustment equation for the population shares of the two groups; a rush towards fundamentalism when the price misalignment becomes too large; and, finally, differently strong noise components in the demand per chartist and fundamentalist trader, which implies a structural stochastic volatility in the returns. The estimation is performed using the method of simulated moments. Combining it with bootstrap and Monte Carlo methods, it is found that the model cannot be rejected by the empirical daily returns from a stock market index and a foreign exchange rate. Measures of the matching of the single moments are satisfactory, too, while the behavioural parameters are well identified and are able to discriminate between the two markets.

Leverage regulation: An agent-based simulation

An agent-based financial market model is used to simulate the effects of financial regulation to reduce financial leverage. Results suggest that regulating leverage using margin calls can lead to less frequent financial crises per century, however, it creates harder hit financial crises than without regulation. In addition, regulation where the central authority tries to prick bubbles also leads to less frequent financial crises, but, creates greater volatility. Lastly, I find that leverage regulation where agent’s ability to borrow is not dependent on price produces less frequent crises and less volatility than the other regimes.

Transaction Taxes and Traders with Heterogeneous Investment Horizons in an Agent-Based Financial Market Model

AbstractThis agent-based financial market model is a generalization of the model of Westerhoff (The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies) by traders who are allowed to have different investment horizons as introduced by Demary (Who Does a Currency Transaction Tax Harm More: Short-Term Speculators or Long-Term Investors?). Our research goals are, first, to study what consequences the introduction of heterogeneous investment horizons has for agent-based financial market models, and second, how effective transaction taxes are in stabilizing financial markets. Numerical simulations reveal that under sufficiently small tax rates traders abstain from short-term trading in favour of longer investment horizons. This change in behavior leads to less volatility and less mispricings. When the tax rate exceeds a certain threshold, however, mispricings increase as also found in Westerhoff (Heterogeneous Traders and the Tobin Tax and The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies). This emergent property is due to the fact that taxation reduces short-term fluctuations and causes longer lasting trends in the exchange rate. As a result, the longer term fundamentalist trading rule becomes unpopular in favor of the longer term trend-chasing rule.