INFORMATION DYNAMICS IN FINANCIAL MARKETS

A noisy rational expectations model of asset trading is extended to incorporate costs of information acquisition and expectation formation. Because of the information costs, how much information to acquire becomes an important decision. Agents make this decision by choosing an expectations strategy about the future value of information. Because expectation formation is costly, agents often choose strategies that are simpler (and thus cheaper) than rational expectations. The model's dynamics can be expressed in terms of the market precision, which represents the amount of information acquired by the average agent. Under certain conditions, market precision follows an unstable and highly irregular time path. This irregularity directly affects observable market quantities. In particular, simulated time series for return volatility and trading volume display a copersistence similar to that found in actual financial data.

[1]  Peter E. Rossi,et al.  Bayesian Analysis of Stochastic Volatility Models , 1994 .

[2]  André de Palma,et al.  Discrete Choice Theory of Product Differentiation , 1995 .

[3]  E. Fama,et al.  Efficient Capital Markets : II , 2007 .

[4]  George W. Evans,et al.  Expectation Calculation and Macroeconomic Dynamics , 1992 .

[5]  T. Andersen Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility , 1996 .

[6]  P. Clark A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices , 1973 .

[7]  Robert M. Townsend,et al.  Forecasting the Forecasts of Others , 1983, Journal of Political Economy.

[8]  J. Goeree,et al.  Heterogeneous beliefs and the non-linear cobweb model , 2000 .

[9]  Peter E. Rossi,et al.  Stock Prices and Volume , 1992 .

[10]  M. Bray,et al.  Rational Expectations Equilibria, Learning, and Model Specification , 1986 .

[11]  Jiang Wang,et al.  Trading Volume and Serial Correlation in Stock Returns , 1992 .

[12]  L. Summers,et al.  Noise Trader Risk in Financial Markets , 1990, Journal of Political Economy.

[13]  L. Blume The Statistical Mechanics of Strategic Interaction , 1993 .

[14]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

[15]  Blake LeBaron,et al.  A Dynamic Structural Model for Stock Return Volatility and Trading Volume , 1995 .

[16]  R. Palmer,et al.  Time series properties of an artificial stock market , 1999 .

[17]  V. Madrigal,et al.  Testing Financial Market Equilibrium under Asymmetric Information , 1992, Journal of Political Economy.

[18]  Jiang Wang,et al.  A Model of Competitive Stock Trading Volume , 1994, Journal of Political Economy.

[19]  H. Föllmer Random economies with many interacting agents , 1974 .

[20]  Christopher G. Lamoureux,et al.  Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects , 1990 .

[21]  Sanford J. Grossman On the Impossibility of Informationally Efficient Markets , 1980 .

[22]  M. Bray Learning, estimation, and the stability of rational expectations , 1982 .

[23]  Martin Hellwig,et al.  On the aggregation of information in competitive markets , 1980 .

[24]  John R. Koza,et al.  Hidden Order: How Adaptation Builds Complexity. , 1995, Artificial Life.

[25]  Jonathan M. Karpoff The Relation between Price Changes and Trading Volume: A Survey , 1987, Journal of Financial and Quantitative Analysis.

[26]  R. Marimon Learning from learning in economics , 1996 .

[27]  Blake LeBaron,et al.  A Dynamic Structural Model for Stock Return Volatility and Trading Volume , 1995 .

[28]  Masanao Aoki,et al.  Economic Fluctuations With Interactive Agents: Dynamic And Stochastic Externalities , 1995 .

[29]  Market efficiency : stock market behaviour in theory and practice , 1997 .

[30]  R. Palmer,et al.  Asset Pricing Under Endogenous Expectations in an Artificial Stock Market , 1996 .

[31]  W. Arthur,et al.  The Economy as an Evolving Complex System II , 1988 .

[32]  W. Arthur,et al.  Complexity in Economic and Financial Markets , 1995 .

[33]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[34]  William A. Brock,et al.  A rational route to randomness , 1997 .

[35]  T. Sargent Bounded rationality in macroeconomics , 1993 .

[36]  Wagner A. Kamakura,et al.  Book Review: Structural Analysis of Discrete Data with Econometric Applications , 1982 .