Evolution and Time Horizons in an Agent-Based Stock Market
Recent 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.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||01 Mar 1999|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://fmwww.bc.edu/CEF99/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- J. Doyne Farmer, 1998.
"Market Force, Ecology, and Evolution,"
Research in Economics
98-12-117e, Santa Fe Institute.
- G. Caldarelli & M. Marsili & Y. -C. Zhang, 1997. "A Prototype Model of Stock Exchange," Papers cond-mat/9709118, arXiv.org.
- William A. Brock & Cars H. Hommes, 1997.
"A Rational Route to Randomness,"
Econometric Society, vol. 65(5), pages 1059-1096, September.
- James Bullard & John Duffy, 1998.
"Learning and excess volatility,"
1998-016, Federal Reserve Bank of St. Louis.
- 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.
- Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
- Campbell, John & Viceira, Luis, 1999.
"Consumption and Portfolio Decisions When Expected Returns are Time Varying,"
3163266, Harvard University Department of Economics.
- 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, 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, 1998. "Consumption and Portfolio Decisions When Expected Returns Are Time Varying," Harvard Institute of Economic Research Working Papers 1835, Harvard - Institute of Economic Research.
- 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.
- 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.
- C. Busshaus & H. Rieger, 1999. "A prognosis oriented microscopic stock market model," Papers cond-mat/9903079, arXiv.org.
- 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.
- 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.
- Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
- repec:cup:cbooks:9780521296762 is not listed on IDEAS
- 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.
- Carl Chiarella, 1992. "The Dynamics of Speculative Behaviour," Working Paper Series 13, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
- 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.
- 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.
- Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
- Rajesh Chakrabarti, 1999. "Just Another Day in the Inter-bank Foreign Exchange Market," Computing in Economics and Finance 1999 652, Society for Computational Economics.
When requesting a correction, please mention this item's handle: RePEc:sce:scecf9:1342. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.