The value of information in a multi-agent market model

Abstract.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.

[1]  Ronald H. Rnndles.  Nonparametric Statistical Inference (2nd ed.) , 1986 .

[2]  E. Ziegel.  Introduction to the Theory and Practice of Econometrics , 1989 .

[3]  Martin Hellwig,et al.  Rational expectations equilibrium with conditioning on past prices , 1982 .

[4]  R. Cont.  Empirical properties of asset returns: stylized facts and statistical issues , 2001 .

[5]  S. Sunder.  MARKET FOR INFORMATION: EXPERIMENTAL EVIDENCE' , 1992 .

[6]  B. Malkiel.  The Ef cient Market Hypothesis and Its Critics , 2003 .

[7]  M. C. Jensen.  The Performance of Mutual Funds in the Period 1945-1964 , 1967 .

[8]  M. Marchesi,et al.  Journal of economic behavior and organization: special issue on heterogeneous interacting agents in financial markets , 2002 .

[9]  F. A. Hayek.  The American Economic Review , 2007 .

[10]  B. Malkiel.  Passive Investment Strategies and Efficient Markets , 2003 .

[11]  J. Gibbons,et al.  Nonparametric Statistical Inference, 2nd Edition. , 1986 .

[12]  Paolo Pellizzari,et al.  Fundamentalists clashing over the book: a study of order-driven stock markets , 2003 .

[13]  Stephen Figlewski.  Information Diversity and Market Behavior , 1982 .

[14]  Jean Dickinson Gibbons,et al.  Nonparametric Statistical Inference. 2nd Edition. , 1986 .

[15]  Wolfgang Breymann,et al.  Dependence structures for multivariate high-frequency data in finance , 2003 .

[16]  A. Cowles.  Can Stock Market Forecasters Forecast , 1933 .

[17]  M. Raberto,et al.  Fraudulent Agents in an Artificial Financial Market , 2003, cond-mat/0310036.

[18]  Jstor.  The journal of economic perspectives , 1987 .