Santa Fe Artificial Stock Market Research Articles

Revisiting the issue of survivability and market efficiency with the Santa Fe Artificial Stock Market

The relevance of risk preference and forecasting accuracy for investor survival has recently been the focus of a series of theoretical and simulation studies. At one extreme, it has been proven that risk preference can be entirely irrelevant (Sandroni in Econometrica 68:1303–1341, 2000; Blume and Easley in Econometrica 74(4):929–966, 2006). However, the agent-based computational approach indicates that risk preference matters and can be more relevant for survivability than forecasting accuracy (Chen and Huang in Advances in natural computation, Springer, Berlin, 2005; J Econ Behav Organ 67(3):702–717, 2008; Huang in J Econ Interact Coord, 2015). Chen and Huang (Inf Sci 177(5):1222–1229, 2007, 2008) further explained that it is the saving behavior of traders that determines their survivability. However, institutional investors do not have to consider saving decisions that are the most influential investors in modern financial markets. Additionally, traders in the above series of theoretical and simulation studies have learned to forecast the stochastic process that determines which asset will pay dividends, not the market prices and dividends. To relate the research on survivability to issues with respect to the efficient markets hypothesis, it is better to endow agents with the ability to forecast market prices and dividends. With the Santa Fe Artificial Stock Market, where traders do not have to consider saving decisions and can learn to forecast both asset prices and dividends, we revisit the issue of survivability and market efficiency. We find that the main finding of Chen and Huang (2008) that risk preference is much more relevant for survivability than forecasting accuracy still holds for a wide range of market conditions but can fail when the baseline dividend becomes very small. Moreover, the advantage of traders who are less averse to risk is revealed in the market where saving decisions are not taken into account. Finally, Huang’s (2015) argument regarding the degree of market inefficiency is confirmed.

A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach

The market microstructure literatures study how the traders work in the financial market. In this paper, we propose a novel heterogeneous agent-based multi-asset artificial stock market based on Santa Fe Artificial Stock Market (SFI-ASM) to compare the financial market microstructure between U.S. and China. We first develop a set of new parameters for the single stock market simulation to improve the way that agents monitor the market and choose different strategies, which make our model closer to the real financial market. Secondly, we construct a multiple assets financial market by incorporating two new types of agents, namely, zero-intelligence agents and less-intelligence agents, and conduct simulations for different evolution speeds, strategies, and intelligence levels to achieve the optimal models of Chinese and U.S. financial markets before and after the financial crisis. Based on the simulation results, we present a comprehensive analysis of the market microstructure for the two financial markets.

Technical trading in the Santa Fe Institute Artificial Stock Market revisited

This paper reconsiders evidence of technical trading in the Santa Fe Artificial Stock Market in light of an upwardly biased mutation operator. While an alternative mutation operator points to no technical trading, two tests other than the inappropriate investigation of the aggregate bit level establish the existence of technical trading beyond a doubt. The following analysis of the two mutation operators identifies genetic drift as an important evolutionary force and provides tools to judge the relative importance of selection and genetic drift with arbitrary mutation operators. It also finds that when selective forces are weak, the continued existence of technical trading can be reconciled with the Efficient Market Hypothesis.

Agent-Based Modeling

The Santa Fe Artificial Stock Market (ASM) is a well-known agent-based model. A new revision of the code (ASM-2.2) is now available. This article describes some of the changes that were made in the code base and also presents some important lessons for agent-based modelers that can be illustrated with the code. Because the code is available on the Internet, it is hoped that this discussion and the code base it represents will be helpful to people who are planning projects in the field of agent-based modeling.

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.

Financial Markets can be at Sub-Optimal Equilibria

We use game theory and Santa Fe Artificial Stock Market, an agent-based model of an evolving stock market, to study the optimal frequency for traders to revise their market forecasting rules. We discover two things: There is a unique strategic Nash equilibrium in the game of choosing forecast revision rates, and this equilibrium is sub-optimal in the sense that traders' earnings are not maximized an the market is inefficient. This strategic equilibrium is due to an analogue of the prisoner's dilemma; the optimal global state is unstable because each trader has too much incentive to `defect' and use forecasting rules that pull the market into thesub-optimal equilibrium.

Fuzzy inductive reasoning, expectation formation and the behavior of security prices

This paper extends the Santa Fe Artificial Stock Market Model (SFASM) studied by LeBaron, Arthur and Palmer (1999, Journal of Economic Dynamics and Control 23, 1487–1516) in two important directions. First, some might question whether it is reasonable to assume that traders are capable of handling a large number of rules, each with numerous conditions, as is assumed in the SFASM. We demonstrate that similar results can be obtained even after severely limiting the reasoning process. We show this by allowing agents the ability to compress information into a few fuzzy notions which they can in turn process and analyze with fuzzy logic. Second, LeBaron et al. have reported that the kurtosis of their simulated stock returns is too small as compared to real data. We demonstrate that with a minor modification to how traders go about deciding which of their prediction rules to rely on when making demand decisions, the model can in fact produce return kurtosis that is comparable to that of actual returns data.

An artificial stock market

The Santa Fe Artificial Stock Market consists of a central computational market and a number of artificially intelligent agents. The agents choose between investing in a stock and leaving their money in the bank, which pays a fixed interest rate. The stock pays a stochastic dividend and has a price which fluctuates according to agent demand. The agents make their investment decisions by attempting to forecast the future return on the stock, using genetic algorithms to generate, test, and evolve predictive rules. The artificial market shows two distinct regimes of behavior, depending on parameter settings and initial conditions. One regime corresponds to the theoretically predicted rational expectations behavior, with low overall trading volume, uncorrelated price series, and no possibility of technical trading. The other regime is more complex, and corresponds to realistic market behavior, with high trading volume, high intermittent volatility (including GARCH behavior), bubbles and crashes, and the presence of technical trading. One parameter that can be used to control the regime is the exploration rate, which governs how rapidly the agents explore new hypotheses with their genetic algorithms. At a low exploration rate the market settles into the rational expectations equilibrium. At a high exploration rate it falls into the more realistic complex regime. The transition is fairly sharp, but close to the boundary the outcome depends on the agents’ initial “beliefs”—if they believe in rational expectations they occur and are a local attractor; otherwise the market evolves into the complex regime.

Asset Pricing Under Endogenous Expectations in an Artificial Stock Market

We propose a theory of asset pricing based on heterogeneous agents who continually adapt their expectations to the market that these expectations aggregatively create. And we explore the implications of this theory computationally using our Santa Fe artificial stock market.