Information Dissemination and Aggregation in Asset Markets with Simple Intelligent Traders (original) (raw)
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Documentos de trabajo (FEDEA), 2008
We study with the help of a laboratory experiment the conditions under which an uninformed manipulator -a robot trader that unconditionally buys several shares of a common value asset in the beginning of a trading period and unwinds this position later on -is able to induce higher asset prices. We find that the average contract price is significantly higher in the presence of the manipulator if, and only if, the asset takes the lowest possible value and insiders have perfect information about the true value of the asset. It is also evidenced that the robot trader makes trading gains; i.e., independently on whether the informed traders have perfect or partial information, it earns always more than the average trader. Finally, not only uninformed subjects suffer from the presence of the robot trader, but also some of the imperfectly informed insiders have lower payoffs once the robot trader is added as a market participant.
Agent-Based Models of Financial Markets: A Comparison with Experimental Markets
SSRN Electronic Journal, 2001
We construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among arti cially-intelligent traders endowed with varying degrees of learning capabilities. In the course of six di erent experimental designs, we i n vestigate a number of features of our agent-based model: the price e ciency of the market, the speed at which prices converge to the rational expectations equilibrium price, the dynamics of the distribution of wealth among the di erent types of AI-agents, trading volume, bid/ask spreads, and other aspects of market dynamics. We are able to replicate several ndings of human-based experimental markets, however, we also nd intriguing di erences between agent-based and human-based experiments.
Today information is generally considered the most valuable good in modern economies. Especially in financial markets information is often viewed as the only ingredient necessary to achieve above-average returns. However, empirical, theoretical and experimental work shows that the matter is not that simple. We develop an experimental setting to analyse how valuable forecasting ability is in financial markets. We find that knowledge about the future development of the profits of a company does no necessarily improve the performance of an agent in the market. Our experimental markets show similar behaviour to real markets in several very important aspects, namely volatility clustering, excess kurtosis, and the autocorrelation behaviour. This increases our confidence, that the one feature not observable in real markets - the relation between information level and return - looks similar to our results as well.
Information aggregation in experimental asset markets in the presence of a manipulator
Documentos de trabajo (FEDEA), 2008
We study with the help of a laboratory experiment the conditions under which an uninformed manipulator - a robot trader that unconditionally buys several shares of a common value asset in the beginning of a trading period and unwinds this position later on - is able to induce higher asset prices. We find that the average contract price is significantly higher in the presence of the manipulator if, and only if, the asset takes the lowest possible value and insiders have perfect information about the true value of the asset. It is also evidenced that the robot trader makes trading gains; i.e., independently on whether the informed traders have perfect or partial information, it earns always more than the average trader. Finally, not only uninformed subjects suffer from the presence of the robot trader, but also some of the imperfectly informed insiders have lower payoffs once the robot trader is added as a market participant.
The value of information in a multi-agent market model
The European Physical Journal B, 2007
We present an experimental and simulated model of a multi-agent stock market driven by a double auction order matching mechanism. Studying the effect of cumulative information on the performance of traders, we find a non monotonic relationship of net returns of traders as a function of information levels, both in the experiments and in the simulations. Particularly, averagely informed traders perform worse than the non informed and only traders with high levels of information (insiders) are able to beat the market. The simulations and the experiments reproduce many stylized facts of tick-by-tick stock-exchange data, such as fast decay of autocorrelation of returns, volatility clustering and fat-tailed distribution of returns. These results have an important message for everyday life. They can give a possible explanation why, on average, professional fund managers perform worse than the market index. PACS. 89.65.Gh Economics; econophysics, financial markets, business and management -89.65.-s Social and economic systems -89.70.+c Information theory and communication theory -89.75.-k Complex systems 1 Introduction "We live in an information society" is a commonly used phrase today. Education, knowledge and information are 2 Bence Tóth, Enrico Scalas, Jürgen Huber, Michael Kirchler: The value of information in a multi-agent market model considered to be the most important ingredients to success in business. While we generally agree with this notion, we think that it does not always hold for financial markets. 70 years ago Cowles [1] was the first to find that the vast majority of stock market forecasters and fund managers are not able to beat the market. Subsequent studies by Jensen [2] and Malkiel [3,4] confirmed this finding. On average about 70 percent of actively managed stock market funds are outperformed by the market, for bonds the number is even higher at 90 percent. Passive investment yields on average 1.5 percent per annum more than an actively managed fund [3]. How can we explain that the highly paid, professionally trained and, above all, well informed specialists managing these funds are not able to perform better than the market? The question whether more information is always good for market participants is highly relevant not only for fund managers, investment banks and regulators, but for every individual investor as well. In this paper we present results from experimental and simulation studies which allow improving our understanding of the relationship between information and investment success in markets. Our model features several innovations: First, our model is a multi-period model and therefore dynamic. It thereby overcomes one of the major weaknesses of earlier research relying only on static environments. Second, we use several information levels instead of only two used in most of the literature on the topic (e.g. Refs. [5,). This is critical to go beyond the straightforward (and not surprising) result that insiders are able to outperform uninformed investors. As we will see the most interesting cases lie between these extremes. The averagely informed traders are the ones we are most interested in, as they exhibit underperformance in our experiments.
SSRN Electronic Journal, 2019
Information dissemination and aggregation are key economic functions of financial markets. How intelligent do traders have to be for the complex task of aggregating diverse information (i.e., approximate the predictions of the rational expectations equilibrium) in a competitive double auction market? An apparent ex-ante answer is: intelligent enough to perform the bootstrap operation necessary for the task-to somehow arrive at prices that are needed to generate those very prices. Constructing a path to such equilibrium through rational behavior has remained beyond what we know of human cognitive abilities. Yet, laboratory experiments report that profit motivated human traders are able to aggregate information in some, but not all, market environments (Plott and Sunder 1988, Forsythe and Lundholm 1990). Algorithmic agents have the potential to yield insights into how simple individual behavior may perform this complex market function as an emergent phenomenon. We report on a computational experiment with markets populated by algorithmic traders who follow cognitively simple heuristics humans are known to use. These markets, too, converge to rational expectations equilibria in environments in which human markets converge, albeit slowly and noisily. The results suggest that high level of individual intelligence or rationality is not necessary for efficient outcomes to emerge at the market level; the structure of the market itself is a source of rationality observed in the outcomes.
2002
In this paper, we present a model that simulates the behaviour of a heterogenous collection of financial traders on a market. Each trader is modelled as an autonomous, interactive agent and the agregation of their behavior results in market behaviour. We specifically look at the role of information arriving at the market and the influence of heterogeneity on market dynamics. The main conclusions are that the quality of the information determines how the market will behave and secondly, heterogeneity is required in order to find the right statistical properties of the price and return time series.
2013
We construct an agent-based computer simulated financial market. Trading in this market is not continuous. The market price is formed using a limit-order book. The modelled investors receive biased information and they attempt to maximize their wealth. Different traders, from noise to chartist and informed, coexist in the same market. We show how stylized facts can be formed by the presence of chartists or a simple lag in investor information. Price bubbles can arise when market prices are dominated by technical traders. Interestingly we show that well informed investors can earn more if the adopt, in special situations, a technical strategy. Using our results we propose a new theorem for market dynamics called “sometimes efficient markets”.
On Intelligent-Agent Based Analysis of Financial Markets
Agent-based computational economics acknowledges the distributed nature of trading in financial markets by modeling the markets as evolving systems of autonomous, interacting agents that correspond to the trading parties. Conventionally, the behavior of traders has been described mathematically, and the market system is analyzed at equilibrium conditions. The dynamics of price formation, however, is influenced by the large diversity in the cognitive structures of the traders (e.g. differences in decision making methods, interpretation of available information and learning capacity), their specific circumstances (e.g. attitude to risk, time horizon) and the organization of the specific market in which the traders operate (e.g. market microstructure). Therefore, we propose to study financial markets by using intelligent agents that have rich cognitive structures borrowed from artificial intelligence research for modeling their decision making behavior. This representation allows us to model the decision making behavior of agents in terms of algorithms, that can represent a more diverse set of behaviors than mathematical formulae only. We discuss the role of intelligent-agents in the analysis of financial markets and speculate on the type of agents that can be expected to be suitable for the analysis and simulation of financial markets. We elucidate our thoughts by exposing the outline of a research project that has started recently at our university. As a first step of our research project, we discuss a classification of adaptation that we proposed recently for agents in agent-based computational economics.
A multi-agent system for analyzing the effect of information on prediction markets
International Journal of Intelligent Systems, 2011
Prediction markets have been shown to be a useful tool for forecasting the outcome of future events by aggregating public opinion about the event's outcome. In this paper, we investigate an important aspect of prediction markets—the effect of different information-related parameters on the behavior of the traders in the market. We have developed a multi-agent based system that incorporates different information-related aspects including the arrival rate of information, the reliability of information, the penetration or accessibility of information among the different traders, and the perception or impact of information by the traders. We have performed extensive simulations of our agent-based prediction market for analyzing the effect of information-related parameters on the traders' behaviors expressed through their trading prices, and compared our agents' strategies with another agent-based pricing strategy used in prediction markets called the zero intelligence strategy. Our results show that information-related parameters have a significant impact on traders' beliefs about event outcomes, and, frequent, reliable information about events improves the utilities that the traders receive. Overall, our work provides a better understanding of the effect of information on the operation of prediction markets and on the strategies used by the traders in the market. © 2011 Wiley Periodicals, Inc.