Study on Evolvement Complexity in an Artificial Stock Market

An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit that large events are frequent in the fluctuation of the stock price generated by the present model when compared with a normal process, and the price returns distribution is L\'{e}vy distribution in the central part followed by an approximately exponential truncation. In addition, by defining a variable to gauge the "evolvement complexity" of this system, we have found a phase cross-over from simple-phase to complex-phase along with the increase of the number of individuals, which may be a ubiquitous phenomenon in multifarious real-life systems.Comment: 4 pages and 4 figure

Building an Artificial Stock Market Populated by Reinforcement-Learning Agents

In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup.agent-based financial modelling, artificial stock market, complex dynamical system, emergent properties, market efficiency, agent heterogeneity, reinforcement learning

A Study of Neo-Austrian Economics using an Artificial Stock Market

An agent-based artificial financial market (AFM) is used to study market efficiency and learning in the context of the Neo-Austrian economic paradigm. Efficiency is defined in terms of the 'excess' profits associated with different trading strategies, where excess for an active trading strategy is defined relative to a dynamic buy and hold benchmark. We define an Inefficiency matrix that takes into account the difference in excess profits of one trading strategy versus another ('signal') relative to the standard error of those profits ('noise') and use this statistical measure to gauge the degree of market efficiency. A one-parameter family of trading strategies is considered, the value of the parameter measuring the relative 'informational' advantage of one strategy versus another. Efficiency is then investigated in terms of the composition of the market defined in terms of the relative proportions of traders using a particular strategy and the parameter values associated with the strategies. We show that markets are more efficient when informational advantages are small (small signal) and when there are many coexisting signals. Learning is introduced by considering 'copycat' traders that learn the relative values of the different strategies in the market and copy the most successful one. We show how such learning leads to a more informationally efficient market but can also lead to a less efficient market as measured in terms of excess profits. It is also shown how the presence of exogeneous information shocks that change trader expectations increases efficiency and complicates the inference problem of copycats.Neoaustrian economics, Market efficiency, Artificial financial market, Learning, Adaptation

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.Comment: 15 pages, 9 figures, Physica A, 201

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.This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico 306331/2014-4 to Professor Newton da Costa Jr.; and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior to Dr Wlademir Prates

An Artificial Stock Market with Interaction Network and Mimetic Agents

Agent-based artificial stock markets attracted much attention over the last years, and many models have been proposed. However, among them, few models take into account the social interactions and mimicking behaviour of traders, while the economic literature describes investors on financial markets as influenced by decisions of their peers and explains that this mimicking behaviour has a decisive impact on price dynamics and market stability. In this paper we propose a continuous double auction model of financial market, populated by heterogeneous traders who interact through a social network of influence. Traders use different investment strategies, namely: fundamentalists who make a decisions based on the fundamental value of assets; hybrids who are initially fundamentalists, but switch to a speculative strategy when they detect an uptrend in prices; noise traders who don’t have sufficient information to take rational decisions, and finally mimetic traders who imitate the decisions of their mentors on the interactions network. An experimental design is performed to show the feasibility and utility of the proposed model

The Santa Fe Artificial Stock Market Re-Examined - Suggested Corrections

This paper rectifies a design problem in the Santa Fe Artificial Stock Market Model. Due to a faulty mutation operator, the resulting bit distribution in the classifier system was systematically upwardly biased, thus suggesting increased levels of technical trading for smaller GA-invocation intervals. The corrected version partly supports the Marimon-Sargent-Hypothesis that adaptive classifier agents in an artificial stock market will always discover the homogeneous rational expectation equilibrium. While agents always find the correct solution of non-bit usage, analyzing the time series data still suggests the existence of two different regimes depending on learning speed. Finally, classifier systems and neural networks as data mining techniques in artificial stock markets are discussed.Asset Pricing; Learning; Financial Time Series; Genetic Algorithms; Classifier Systems; Agent-Based Simulation