Artificial Stock Market Research Articles

Computational Experiments for Complex Social Systems—Part III: The Docking of Domain Models

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber–physical–social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.

Noise traders in an agent-based artificial stock market

AbstractThis paper investigates whether noise traders can survive in the long run and how they influence financial markets by proposing an agent-based artificial stock market, as one simulation model of computational economics. This market contains noise traders, informed and uninformed traders. Informed and uninformed traders can learn from information by using Genetic Programming, while noise traders cannot. The system is first calibrated to real financial markets by replicating several stylized facts. We find that noise traders cannot survive or they just transform to other kind of traders in the long run, and they increase market volatility, price distortion, noise trader risk, and trading volume in the market. However, regulation intervention, e.g., price limits, transaction tax and longer settlement cycle, can affect noise trader’s surviving period and their influence on markets.

Does more intelligent trading strategy win? Interacting trading strategies: an agent-based approach

An artificial financial market is built on top of the Genoa Artificial Stock Market. The market is populated with agents having different trading strategies and they are let to interact with each other. Agents differ in the trading method they use to trade, and they are grouped as noise, technical, statistical analysis, and machine learning traders. The model is validated by the replication of stylized facts in financial asset returns. We were able to replicate the leptokurtic shape of the probability density function, volatility clustering, and the absence of autocorrelation in asset returns. The wealth dynamics for each agent group are analyzed throughout the trading period. Agents with a higher time complexity trading strategy outperform those with a strategy comparing their final wealth.

A complex adaptive agent modeling to predict the stock market prices

Understanding the behaviors of financial markets and their participants remains a challenging problem to resolve. Adaptive agents, which switch from fundamentalist to chartist behavior, are examined in some recent work. In this paper, we propose an adaptive agent-based model that combines three forecasting behaviors of financial agents: fundamentalist, chartist, and mimetic. The weighting of each type of behavior in the final forecasting changes according to the market cycle. Our model adapts to the different cycles of the market.We assess the ability of the proposed model to predict and explain the dynamics of stock market price formation. First, on the microscopic level, we consider four agent-based models: fundamentalist, chartist, mimetic and adaptive. We compare the prices generated by the different prediction models to the real prices generated by the US market (S&P 500 index) over the period (1990–2021). Second, at a macroscopic level, we set up a multi-agent system to simulate an artificial stock market composed of the four types of agents with different market fractions. We compare the prices generated by the artificial market with the real data generated by the US market.The series of statistical analyses that we have carried out allows us to conclude that: the proposed adaptive agent exists in the stock market, he offers a better accuracy of price predictions compared to fundamentalist, chartist and mimetic models and that he can explain the dynamics of the stock market prices formation when he dominates.

The Cross Section of Stock Returns in an Artificial Stock Market

The Cross Section of Stock Returns in an Artificial Stock Market

An adaptive regression for agent-based modeling

The article discusses an algorithm, which that can be used to implement adaptive behavior of agents in agent-based models (ABM). It is assumed that an agent has some internal parametric model of the surrounding world, which motivates a likelihood function for the information about the world received by the agent. The process of adaptive learning of an agent via changing parameters is presented as filtering in a general state space model. By using a linear Gaussian transition density and a quadratic approximation for the log-likelihood function, an algorithm is obtained, which is called SQ filter in the article. This algorithm is a modification of the classical Kalman filter. It is applied to the linear regression with time-varying parameters. When an agent receives new information, the parameter estimates, which include both the regression coefficients and the error variance, are adjusted adaptively by taking into account possible outliers. The performance of the proposed adaptive regression was tested on two economic ABM. The algorithm showed good results both in an artificial stock market model where trader agents predict the market price and in a model of the Russian economy where firms predict demand for their output. With its help, it is possible to endow agents with plausible behavior without using overly complex calculations.

An Agent-Based Model for the Impact of Price Limit Changes on Market Quality

This paper constructs an artificial stock market, especially the description of Chinese stocks to form a benchmark model of this paper. Then, we examine the relationships between price limit changes and market quality in the Chinese stock market. We set up five boards in the model and adopt the current price limits of the A-share market. In terms of experimental design, this paper adjusts the price limit to explore the path of the reform of the price limit. We also cancel the price limit to explore the necessity of price limits in China’s A-share market. After simulations, the experimental results of this paper show that relaxing the price limit will improve market quality. When we adjust the price limit from 10% to 20%, market volatility increases slightly, market liquidity increases and market pricing efficiency improves. When we cancel the price limit, market volatility increases significantly, market liquidity increases and market pricing efficiency decreases significantly. Our study shows that it is necessary to implement the price limit policy in the Chinese market, and a moderate relaxation of the price range will improve the quality of the market.

Impacts of social networks in an agent-based artificial stock market

ABSTRACT We propose an agent-based artificial stock market to investigate the influences of social networks on financial markets. It contains four types of traders whose information sets and trading strategies are different. The application of artificial intelligence is employed in informed and uninformed traders’behaviour and heterogeneity. When information is exogenous, social networks result in higher volatility and trading volume and lower price distortion and bid-ask spread. When information is endogenous, the influences are reversed. The reason is that social networks harm information production after traders tend to rely on information from communication, instead of spending a cost on it..

Pyramid scheme in stock market: A kind of financial market simulation* *Project supported by the National Natural Science Foundation of China (Grant Nos. 71932008 and 91546201).

Artificial stock market simulation based on agent is an important means to study financial market. Based on the assumption that the investors are composed of a main fund, small trend and contrarian investors characterized by four parameters, we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes. Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors, the small investors’ parameters of taking profit and stopping loss, the order size of the main fund and the strategies adopted by the main fund. Our work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets, design trading rules for regulators and develop trading strategies for investors.

Sentiment Analysis of Artificial Stock Markets

Sentiment Analysis of Artificial Stock Markets

Evaluation of Technical Analysis Trading Rules in a Artificial Stock Market Environment

Artificial multi-agent systems can be applied to replicate complex processes, including the stock market. In this manuscript, an artificial stock market was simulated. Heterogeneous agents are set to trade by using different investiment strategies through a double auction market. The main innovative aspect of this work is to evaluate the performance and effectiveness of agents with different investment strategies based on the combination of Technical/Fundamental Analysis. According to the simulations performed, the results showed that the time series resulting from the interactions among these agents resemble to the real financial series, reproducing some of the stylized facts found in the Literature. Moreover, the statistical analysis reported no significant differences in the distribution mean performance between these diverse agents types.

Investor Sentiment in an Artificial Limit Order Market

This paper examines the under/overreaction effect driven by sentiment belief in an artificial limit order market when agents are risk averse and arrive in the market with different time horizons. We employ agent-based modeling to build up an artificial stock market with order book and model a type of sentiment belief display over/underreaction by following a Bayesian learning scheme with a Markov regime switching between conservative bias and representative bias. Simulations show that when compared with classic noise belief without learning, sentiment belief gives rise to short-term intraday return predictability. In particular, under/overreaction trading strategies are profitable under sentiment beliefs, but not under noise belief. Moreover, we find that sentiment belief leads to significantly lower volatility, lower bid-ask spread, and larger order book depth near the best quotes but lower trading volume when compared with noise belief.

Tick size and market quality: Simulations based on agent‐based artificial stock markets

SummaryThis paper investigates the way that minimum tick size affects market quality based on an agent‐based artificial stock market. Our results indicate that stepwise and combination systems can promote market quality in certain aspects, compared with a uniform system. A minimal combination system performed the best to improve market quality. This is the first study to analyse tick size systems that remain at the theory stage and compare four types of system under the same experimental environment. The results suggests that a minimal combination system could be considered a new direction for market policy reform to improve market quality.

Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model

Based on the multi-agent model, an artificial stock market with four types of traders is constructed. On this basis, this paper focuses on comparing the effects of liquidation behavior on market liquidity, volatility, price discovery efficiency and long memory of absolute returns when the institutional trader adopts equal-order strategy, Volume Weighted Average Price (VWAP) strategy and Implementation Shortfall (IS) strategy respectively. The results show the following: (1) the artificial stock market based on multi-agent model can reproduce the stylized facts of real stock market well; (2) among these three algorithmic trading strategies, IS strategy causes the longest liquidation time and the lowest liquidation cost; (3) the liquidation behavior of institutional trader will significantly reduce market liquidity, price discovery efficiency and long memory of absolute returns, and increase market volatility; (4) in comparison, IS strategy has the least impact on market liquidity, volatility and price discovery efficiency, while VWAP strategy has the least impact on long memory of absolute returns.

Quantifying the concerns of Dimon and Buffett with data and computation

Despite considerable efforts, the determinants of firm growth and financial market volatility have not been definitively identified, yet sets of stylized facts – most notably power-law distributions – relating to firm size and market returns suggest both evolve as part of a complex system. This scenario implies that a positive feedback loop between firms and investors exists and may be responsible for prejudicing the way management allocates their resources, with firm, and economic, growth adversely affected. Further, there are growing real-world concerns that the management of publicly listed firms is becoming too concerned with the movement of their firm's share price, which is adversely influencing resource allocation decisions. A related concern is that agents, in general, within financial markets are placing a disproportionate focus on short-term factors. To investigate the ramifications of the proposed feedback loop on firm growth and market volatility, this paper implements a novel agent-based artificial stock market where management can consider the movements of their firm's endogenously determined share price when allocating resources between sales and margin growth. The results highlight an inferior outcome regarding firm growth, and various other financial metrics, if management is overly concerned with share price movements. The growth of the firms (and market) is also affected by the mixture of the investor classes initiated due to the divergent levels of volatility they create. Additionally, the model presents insights into how and why the extent to which agents consider past outcomes in their decision-making process becomes influential. Notably, the model's results emulate an extensive set of global micro-level firm data. By providing significant insights on the effects of the stock market on management decision-making and its ramifications for firm growth, this paper provides crucial insights into the mechanisms responsible for inefficient behavior by market participants.

Investor Behavior and Risk Contagion in an Information-Based Artificial Stock Market

This paper designs an artificial stock market model to discuss investor behavior and risk contagion caused by market information. Considering the investors’ trading decisions are attributed to the change of market information, new market information no matter from the macro field or the micro field can influence investor behavior and risk contagion. In our artificial stock market model, we assume new information is the sole factor affecting the fluctuation of stock prices. And investor sentiment is influenced by investor sensitivity to new information, investment decisions of neighboring investors, and investor understanding preference for new information. Through simulation experiments, some conclusions can be drawn as follows. Firstly, stock price volatility becomes stronger with increasing investor sensitivity to new information, under the condition of a smaller fundamental contagion coefficient or larger sensitivity to new information from investor neighbors. Secondly, stock price volatility becomes more moderate with increasing sensitivity to new information from investor neighbors, under the condition of a smaller fundamental contagion coefficient or smaller investor sensitivity to new information. Thirdly, with the increasing fundamental contagion coefficient, stock price volatility may strongly increase under the condition of smaller investor sensitivity to new information and smaller sensitivity to new information from investor neighbors, or slightly increase under the condition of smaller investor sensitivity to new information and larger sensitivity to new information from investor neighbors, or slightly decrease under the condition of larger investor sensitivity to new information and smaller sensitivity to new information from investor neighbors, or show a little change under the condition of larger investor sensitivity to new information and larger sensitivity to new information from investor neighbors.

An Agent-Based Approach to Artificial Stock Market Modeling

Abstract Consumer stock markets have long been a target of modeling efforts for the economic gains anticipatorily enabled by well-performing models. Aimed at identifying strategies capable of achieving desired returns, many modeling approaches have attempted to capture the innumerable and intricate complexities present within these adaptive socio-technical systems. Decreasingly constrained by available computation power, contemporary models have grown in sophistication to include several of the features present in de facto market systems. However, these models require extensive effort to dictate the variety of states, behaviors, and adaptations that entities of the system may exhibit. Mandating the development of complex formulas and an incredible number of situational considerations, traditional approaches to stock market modeling are intensive to architect and applicable to a limited range of scenarios. Further, these models commonly fail to incorporate external influences on the actions of investing parties. Employing an agent-based approach, independent and externally influenced entities are modeled to simulate market activity. Under the jurisdiction of assigned simple rules, agents of the system interact in complex and emergent ways without requiring macroscopic guiding equations. Successive trails are conducted using varying initialization values, enabling the determination of robust investment strategies performing well across a range of market scenarios.

Impact of Electronic Liquidity Providers Within a High-Frequency Agent-Based Modeling Framework

The current contribution addresses the impact of high-frequency electronic liquidity provision strategies on the intraday dynamics of financial markets, by means of an artificial stock market. As novel design feature, an event-based intraday time implementation is proposed, allowing for the generation of time-stamped intraday events, which make possible both the aggregation of time series at various time frequencies, as well as the correct simulation of trading strategies that follow different temporal frequencies, e.g., low- and high-frequency. We provide new insights with respect to the determinants of extreme events, such as flash crashes. Finally, we compare the causal chains and the effectiveness of two potential regulatory policies under the same market circumstances, i.e., minimum resting time and financial-transaction taxes, not only with respect to their flash crash prevention power, but also regarding their impact on market participants and market quality, shedding new light on the policy trade-offs.

Propensity to sell stocks in an artificial stock market.

This experimental study of an artificial stock market investigates what explains the propensity to sell stocks and thus the disposition effect. It is a framed field experiment that follows the steps of a previous observational study of investor behavior in the Finnish stock market. Our experimental approach has an edge over the observational study in that it can control extraneous variables and two or more groups can be compared. We consider in particular the groups of amateur students and professional investors because it is well established in the literature that the disposition effect is less pronounced in professionals. The disposition effect was measured by both the traditional metric and a broader one that properly considers return intervals. A full logit model with control variables was employed in the latter case. As a result, we replicate for the broader definition what already has been found for the traditional measure: that investor experience dampens the disposition effect. Trades with positive returns exhibited higher propensity to sell than trades with negative returns. For the overall sample of participants, we find the disposition effect cannot be explained by prospect theory, but we cast doubt on this stance from partitions of data from amateurs and professionals.

Understanding How Short‐Termism and a Dynamic Investor Network Affects Investor Returns: An Agent‐Based Perspective

The unexplained and inconsistent behavior of financial markets provides the motivation to engage interdisciplinary approaches to understand its intricacies better. A proven approach is to consider investors as heterogeneous interacting agents who form information networks to inform their investment decisions. The rationale is that the topology of these networks has contributed to a better understanding of the erratic behavior of financial markets. Introducing investor heterogeneity also allows researchers to identify the characteristics of higher performing investors and the implications of investors exhibiting short‐termism, a feature recognized by some as detrimental to the performance of the economy. To address these topics, an agent‐based artificial stock market is implemented, where investors utilize various information sources, including advice from investors in their network, to inform their investment decisions. Over time investors update their trust in their information sources and evolve their network by connecting to outperforming investors—Oracles—and discarding poor advisers, thereby simulating the evolution of an investor network. The model’s most significant finding is uncovering how the market’s behavior is materially affected by the time‐horizon of investors, with short‐term behavior resulting in greater volatility in the market. Another finding is the reason why short‐term investors generally outperform their long‐term counterparts, particularly in more volatile environments. By providing significant insights into the formation of an investor network and its ramifications for market volatility and wealth creation (destruction), this paper provides crucial clues regarding the empirical data that needs to be collected, assessed, and tracked to ensure policymakers and investors better understand the dynamics of financial markets.