Artificial Stock Research Articles

Adaptive Behavior and Strategy Switching

Using the Agent-based Computational Finance (ACF) method, we build an artificial stock market with heterogeneous adaptive investors and investigate the evolutionary and interacting relationship between rational investors and irrational investors. We find that with strategy switching, there is a symbiosis among the three kinds of investors in the ACF experiments, although the rational investors are often dominant in the market. And our main findings are robust with agents' scale. When the initial values change, the market ecology achieves new equilibrium.

Impact of information cost and switching of trading strategies in an artificial stock market

This paper studies the switching of trading strategies and its effect on the market volatility in a continuous double auction market. We describe the behavior when some uninformed agents, who we call switchers, decide whether or not to pay for information before they trade. By paying for the information they behave as informed traders. First we verify that our model is able to reproduce some of the stylized facts in real financial markets. Next we consider the relationship between switching and the market volatility under different structures of investors. We find that there exists a positive relationship between the market volatility and the percentage of switchers. We therefore conclude that the switchers are a destabilizing factor in the market. However, for a given fixed percentage of switchers, the proportion of switchers that decide to buy information at a given moment of time is negatively related to the current market volatility. In other words, if more agents pay for information to know the fundamental value at some time, the market volatility will be lower. This is because the market price is closer to the fundamental value due to information diffusion between switchers.

The J-Curve and Transaction Taxes: Insights from an Artificial Stock Market

We investigate the distribution of relative returns (RR) among agents who possess varying levels of information in an artificial stock market (ASM). We demonstrate the existence of the J-curve in this market. In contrast to previous studies, the agents in possession of the least information are statistically not different net losers. Moreover, we find that the J-curve is not valid if the relative number of random traders is high. We introduce Tobin-like transaction taxes and show that they destroy liquidity and harm market efficiency. With high taxes, the inequality between agents possessing varying levels of information decreases. However, tax levels dealt with in recent studies influence the market parameters and the J-curve only marginally.

Information identification in different networks with heterogeneous information sources

Traditional cheap-talk game model with homogeneous information sources provided a conclusion that dishonest information sources will not be identified if he changes strategy stochastically. In this paper, the authors incorporate different information diffusion networks and heterogeneous information sources into an agent-based artificial stock market. The obtained results are different with traditional results that identification ability of uninformed agents has been highly improved with diffusion networks and heterogeneous information sources. Additionally, the authors find uninformed agents can improve identification ability only if there exists a sufficient number of heterogeneous information sources in stock market.

Agent-based Modeling and Investors’ Behavior Explanation of Asset Price Dynamics on Artificial Financial Markets

Standard asset pricing models based on rational expectations and homogeneity have problems explaining the complex and volatile nature of financial markets. The heterogeneity in expectations can lead to market instability and complicate dynamics of prices, which are driven by endogenous market forces. In this sense, we use Agent-based computational approach and more specifically artificial Stock Market modeling to explore the market dynamics from a behavioral perspective. Our aim is to point out that the investors’ irrationality explains various numbers of financial anomalies, especially the phenomena that traditional financials models have never been able to explain. We built a virtual financial market that contains three types of investors: fundamentalists, non-fundamentalist and loss adverse investors. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Fractality of profit landscapes and validation of time series models for stock prices

We apply a simple trading strategy for various time series of real and artificial stock prices to understand the origin of fractality observed in the resulting profit landscapes. The strategy contains only two parameters $p$ and $q$, and the sell (buy) decision is made when the log return is larger (smaller) than $p$ ($-q$). We discretize the unit square $(p, q) \in [0, 1] \times [0, 1]$ into the $N \times N$ square grid and the profit $\Pi (p, q)$ is calculated at the center of each cell. We confirm the previous finding that local maxima in profit landscapes are scattered in a fractal-like fashion: The number M of local maxima follows the power-law form $M \sim N^{a}$, but the scaling exponent $a$ is found to differ for different time series. From comparisons of real and artificial stock prices, we find that the fat-tailed return distribution is closely related to the exponent $a \approx 1.6$ observed for real stock markets. We suggest that the fractality of profit landscape characterized by $a \approx 1.6$ can be a useful measure to validate time series model for stock prices.

Modeling and Simulation of the Artificial Stock Market Trading System

By means of computer simulation technique, this paper builds an artificial sto ck market model consisting of decision making agents. Through the fundamental and technical analysis in the aspects of investors, trading cost, transaction volume, risk-free interest, tick size and price-limit system, the model is used to indicate the volatility and liquid ity. The results show that the stock index time series has obtained the characteristics of steep-peak and heavy-tail, wh ich is in accordance with that of real stock market. It is also found that an appropriate increase of tick size or relax of price limit would help imp rove the market liquidity, but to some extent, increase the market volatility. The irrational behavior of institutional investors and lar ge volume of transaction are also easy to result in stock market fluctuation.

Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns

Many scholars express concerns that herding behaviour causes excess volatility, destabilises financial markets, and increases the likelihood of systemic risk. We use a special form of the Strongly Typed Genetic Programming (STGP) technique to evolve a stock market divided into two groups—a small subset of artificial agents called ‘Best Agents’ and a main cohort of agents named ‘All Agents’. The ‘Best Agents’ perform best in term of the trailing return of a wealth moving average. We then investigate whether herding behaviour can arise when agents trade Dow Jones, General Electric, and IBM financial instruments in four different artificial stock markets. This paper uses real historical quotes of the three financial instruments to analyse the behavioural foundations of stylised facts such as leptokurtosis, non-IIDness, and volatility clustering. We found evidence of more herding in a group of stocks than in individual stocks, but the magnitude of herding does not contribute to the mispricing of assets in the long run. Our findings suggest that the price formation process caused by the collective behaviour of the entire market exhibit less herding and is more efficient than the segmented market populated by a small subset of agents. Hence, greater genetic diversity leads to greater consistency with fundamental values and market efficiency.

Study on the Stability of an Artificial Stock Option Market Based on Bidirectional Conduction

Although stock option markets have grown dramatically over the past several decades, the relation between an option and its underlying asset, especially bidirectional conduction, is not particularly clear. So far, there have been many debates about this topic. We try to investigate this problem from a novel angle: an artificial stock market including a stock option is constructed in this paper. The model includes two parts, one is a stock trade module based on the Santa Fe Institute Artificial Stock Market (SFI-ASM), and the other is an option trade module. In the latter module, three types of option traders are employed. The results show that the model is effective, and experiments illustrate that option markets have a remarkable effect on stock markets. Furthermore, by appending options, the model replicates some stylized properties, such as volatility clustering and GARCH effect, which can be observed in real financial markets.

Distribution and growth of brown trout in pristine headwaters of Central Europe

AbstractThe majority of stream-dwelling salmonid populations in Europe are affected by artificial stocking and the fragmentation of riverine ecosystems. The present study was performed in the unique pristine headwaters of the Otava River in the Elbe catchment area of the Czech Republic. The aim was to investigate the spatial distribution and individual growth pattern of brown trout, Salmo trutta, populations. Twenty sites in two main streams and their tributaries were sampled twice a year (spring and autumn) during the period 2005–2011. The sampling sites were grouped into fourteen so-called synchronised population units within the boundaries of three populations, according to analyses of synchrony in population abundance. The individual growth of juveniles (age-0, age-1) varied between all three spatial units (sampling sites, synchronised population units and populations), while the individual growth of adults (age-2 and older) did not. The distinctiveness regarding individual growth and demographic independence among the synchronised population units and populations indicates their suitability for use as population units for management purposes.

Do price limits hurt the market?

Under an artificial stock market composed of bounded-rational and heterogeneous traders, this paper examines whether or not price limits generate the negative effects on the market. Through testing the volatility spillover hypothesis, the delayed price discovery hypothesis, and the trading interference hypothesis, we find that no evidence of volatility spillover is observed. However, the phenomena of delayed price discovery and trading interference indeed exist, and their significance depends on the level of the price limits.

元江鲤种群遗传多样性

Twelve microsatellite loci were used to investigate genetic diversity and population structure of common carp,Cyprinus carpio from Yuan River(the upper reaches of Red River in Yunnan Province,China).One hundred and ninety two samples of adult common carp were collected in 2011 from five sampling points.A total of 201 alleles were detected over all loci and the number of alleles per locus ranged from 2 to 27.There were 113 low frequencies alleles at the frequencies less than 0.05.Mean number of alleles(NA) ranged from 12.25 to 14.67 per locus per population,effective number of alleles(NE) ranged from 8.28 to 9.73,mean observed heterozygosity(HO) and mean expected heterozygosity(HE) at the 12 loci ranged from 0.7765 to 0.8037,and from 0.7761 to 0.8080,and mean polymorphism information content(PIC) ranged from 0.7534 to 0.7843.Mean number of alleles(NA),mean effective number(NE) of alleles,mean observed heterozygosity(HO),mean expected heterozygosity(HE) and mean polymorphism information content(PIC) were 16.50,11.26,0.7927,0.8049,0.7966 respectively for overall samples.The result indicated that the genetic diversity of common carp from Yuan River was abundant.An analysis on the distribution of genetic variation(AMOVA) indicated that the variation within population was very high(95.87%),while among populations was low(1.69%).The value of fixation index and gene flow were 0.0256,9.5003 respectively for overall samples.Nei′s unbiased measures of genetic identity and genetic distance among populations rang from 0.8632 to 0.9700,0.0305 to 0.1471 respectively.This suggests that there were high genetic similarity among the 5 populations and significant genetic differentiations among the common carp individuals from Yuan River.So,the common carp from Yuan River should be managed by a population management unit.The genetic background of artificial stocking of carp larvae should not only be taken into consideration in fisheries enhancement,but also a system of evaluating the stock enhancement effectiveness should be established to prevent losing genetic biodiversity.

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.

Application of Artificial Stock Market in the Stock Market Tradi Mechanism

Application of Artificial Stock Market in the Stock Market Tradi Mechanism

Analyzing the Pareto Principle Based on the Artificial Stock Market

Analyzing the Pareto Principle Based on the Artificial Stock Market

Stock Prices in an Artificial Stock Market with Optimistic and Pessimistic Agents

This research aims to investigate, through simulation models, how the interaction among agents in an artificial stock market can affect the dynamics of asset prices. Thus, the study follows a different methodology for the analysis of prices by exploring the simulation of agents’ behavior in an artificial stock market. From the defining characteristics of heterogeneous agents, we set up an artificial stock market in which individuals interact, by demanding and supplying assets, driving the price of a stock to an equilibrium value. The results suggest that, under the assumption of utility maximizers agents with different expectations about future dividends, asset prices may under-react. The gradual change of prices observed in the sub-reaction confronts the efficient market hypothesis, in which all information is instantly

MICROSTRUCTURE DYNAMICS AND AGENT-BASED FINANCIAL MARKETS: CAN DINOSAURS RETURN?

This paper formalizes observations made under agent-based artificial stock market models into a concrete hypothesis, which is called the Dinosaur Hypothesis. This hypothesis states that the behavior of financial markets constantly changes and that the trading strategies in a market need to continuously co-evolve with it in order to remain effective. After formalizing the hypothesis, we suggest a testing methodology and run tests under 10 international financial markets. Our tests are based on a framework that we recently developed, which used Genetic Programming as a rule inference engine, and Self-Organizing Maps as a clustering machine for the above rules. However, an important assumption of that study was that maps among different periods were directly comparable with each other. In order to allow this to happen, we had to keep the same clusters throughout the different time periods of our experiments. Nevertheless, this assumption could be considered as strict or even unrealistic. In this paper, we relax this assumption. This makes our model more realistic. In addition, this allows us to investigate in depth the dynamics of market behavior and test for the plausibility of the Dinosaur Hypothesis. The results show that indeed markets' behavior constantly changes. As a consequence, strategies need to continuously co-evolve with the market; if they do not, they become obsolete or dinosaurs.

First Evaluation of Different Captive Rearing Techniques for the Re-Establishment of the Red Legged Partridge Populations

In Tuscany, the red-legged partridge (Alectoris rufa) became extinct at the beginning of the 20th century. Recently, some attempts have been made to re-establish wild populations in Tuscany using farm reared birds, but in most cases the released populations have shown difficulties in reaching sufficient viability, even in areas where the habitat can be considered suitable. Modern technologies for rearing game-birds may be not suitable for reintroduction purposes. For this reason we carried out preliminary research to evaluate the survival of red legged partridges reared under natural condition (Natural) compared to those farm reared (Artificial). Natural rearing occurred in a large pen where birds were allowed to mate freely and to nest. Natural reared birds reached a larger size than artificially-reared birds. Partridges were released in a 7.56 sq km protected area located in the province of Leghorn (western Tuscany, Italy). We followed the fate of 22 young radio-tagged red-legged female partridges (half Natural and half Artificial) and 56 young red-legged partridges (25 Artificial and 31 Natural) marked with different coloured ponchos. The survival of Natural radio-tagged red-legged partridges was double that of the Artificial red-legged partridges. After six months, the re-sighting rate of the Natural poncho-marked birds reached 22.6% whereas none of the Artificial stock was re-sighted. The preliminary results of this research suggest that Natural rearing may be an important tool to improve the success of partridge reintroduction.

Statistical Analysis and Agent-Based Microstructure Modeling of High-Frequency Financial Trading

A simulation of high-frequency market data is performed with the Genoa Artificial Stock Market. Heterogeneous agents trade a risky asset in exchange for cash. Agents have zero intelligence and issue random limit or market orders depending on their budget constraints. The price is cleared by means of a limit order book. A renewal order-generation process is used having a waiting-time distribution between consecutive orders that follows a Weibull law, in line with previous studies. The simulation results show that this mechanism can reproduce fat-tailed distributions of returns without ad-hoc behavioral assumptions on agents. In the simulated trade process, when the order waiting-times are exponentially distributed, trade waiting times are exponentially distributed. However, if order waiting times follow a Weibull law, analogous results do not hold. These findings are interpreted in terms of a random thinning of the order renewal process. This behavior is compared with order and trade durations taken from real financial data.

Analyzing the Pareto Principle Based on the Artificial Stock Market

Analyzing the Pareto Principle Based on the Artificial Stock Market