Evolution and Time Horizons in an Agent-Based Stock Market
AbstractRecent research has shown the importance of time horizons in models of learning in finance. The dynamics of how agents adjust to believe that the world around them is stationary may be just as crucial in the convergence to a rational-expectations equilibrium as getting parameters and model specifications correct in the learning process. This paper explores the process of this evolution in learning and time horizons in a simple agent-based financial market. Trading is done in a market with a single stock in finite supply, paying a stochastic dividend. A risk free asset is available in infinite supply. Agents maximize an infinite-horizon time-separable utility function in each period's consumption. They are required to select from a set of given forecasting/trading rules optimized to past data. Heterogeneity is introduced through the time horizon that they believe is relevant to use in deciding over trading rules. Long horizon agents build relative performance measures looking back into the distant past, while those with short horizons believe that only recent measures of performance are useful for decision making. The price of the risky asset is set to balance current agent demand with its fixed supply at each period. Once the price is endogenously determined, returns are calculated and dividends paid. Agents make consumption decisions and wealth is calculated. Relative wealth affects the market in two ways. First, wealthier individuals are able to move prices by larger amounts. Second, evolution takes place in which less wealthy agents are dropped out of the market and replaced with new ones drawn according to current wealth levels. The horizon lengths of wealthier agents are given more weight in the generation of new agents. The primary objectives of this paper are to understand better the convergence properties of learning with heterogeneous horizons. Several benchmark cases are explored in which a stationary rational-expectations equilibrium exists, and agents should converge to the longest horizon possible. The model is explored to see in which cases this convergence does not occur, and if it does not, what sorts of short-horizon features self-reinforce in agents' short-horizon forecasting models. Also, experiments are performed on the "invadeability" of a group of short-horizon investors to see if they can be invaded by those with long horizons. The paper also briefly addresses two eventual goals. First, the replication of certain features in financial data, such as excess volatility and trading volume phenomena. Second, while his model is strictly computational, some assessments about moving it to an analytic setting are made.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 1342.
Date of creation: 01 Mar 1999
Date of revision:
Other versions of this item:
- LeBaron, Blake, 2001. "Evolution And Time Horizons In An Agent-Based Stock Market," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 225-254, April.
- NEP-ALL-1999-07-12 (All new papers)
- NEP-CMP-1999-08-22 (Computational Economics)
- NEP-EVO-1999-07-12 (Evolutionary Economics)
- NEP-FIN-1999-07-12 (Finance)
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- William A. Brock & Blake D. LeBaron, 1995.
"A Dynamic Structural Model for Stock Return Volatility and Trading Volume,"
NBER Working Papers
4988, National Bureau of Economic Research, Inc.
- Brock, William A & LeBaron, Blake D, 1996. "A Dynamic Structural Model for Stock Return Volatility and Trading Volume," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 94-110, February.
- William A. Brock & Cars H. Hommes, 1997.
"A Rational Route to Randomness,"
Econometric Society, vol. 65(5), pages 1059-1096, September.
- Bullard, James & Duffy, John, 2001.
"Learning And Excess Volatility,"
Cambridge University Press, vol. 5(02), pages 272-302, April.
- Rajesh Chakrabarti, 1999. "Just Another Day in the Inter-bank Foreign Exchange Market," Computing in Economics and Finance 1999 652, Society for Computational Economics.
- J. Doyne Farmer, 2002.
"Market force, ecology and evolution,"
Industrial and Corporate Change,
Oxford University Press, vol. 11(5), pages 895-953, November.
- Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
- Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
- John Y. Campbell & Luis M. Viceira, 1996.
"Consumption and Portfolio Decisions When Expected Returns are Time Varying,"
NBER Working Papers
5857, National Bureau of Economic Research, Inc.
- John Y. Campbell & Luis M. Viceira, 1999. "Consumption And Portfolio Decisions When Expected Returns Are Time Varying," The Quarterly Journal of Economics, MIT Press, vol. 114(2), pages 433-495, May.
- John Y. Campbell & Luis M. Viceira, 1998. "Consumption and Portfolio Decisions When Expected Returns Are Time Varying," Harvard Institute of Economic Research Working Papers 1835, Harvard - Institute of Economic Research.
- Viceira, Luis & Campbell, John, 1999. "Consumption and Portfolio Decisions When Expected Returns are Time Varying," Scholarly Articles 3163266, Harvard University Department of Economics.
- G. Caldarelli & M. Marsili & Y. -C. Zhang, 1997. "A Prototype Model of Stock Exchange," Papers cond-mat/9709118, arXiv.org.
- Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762, October.
- repec:att:wimass:9621 is not listed on IDEAS
- Brock, William A. & Hommes, Cars H., 1998.
"Heterogeneous beliefs and routes to chaos in a simple asset pricing model,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 22(8-9), pages 1235-1274, August.
- Carl Chiarella, 1992. "The Dynamics of Speculative Behaviour," Working Paper Series 13, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
- Egenter, E. & Lux, T. & Stauffer, D., 1999. "Finite-size effects in Monte Carlo simulations of two stock market models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 268(1), pages 250-256.
- C. Busshaus & H. Rieger, 1999. "A prognosis oriented microscopic stock market model," Papers cond-mat/9903079, arXiv.org.
- Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
- Michael W. Brandt, 1999. "Estimating Portfolio and Consumption Choice: A Conditional Euler Equations Approach," Journal of Finance, American Finance Association, vol. 54(5), pages 1609-1645, October.
- Evans, George W & Honkapohja, Seppo, 1995. "Local Convergence of Recursive Learning to Steady States and Cycles in Stochastic Nonlinear Models," Econometrica, Econometric Society, vol. 63(1), pages 195-206, January.
- Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, 02.
- Bußhaus, Christian & Rieger, Heiko, 1999. "A prognosis oriented microscopic stock market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 267(3), pages 443-452.
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