Summary
This chapter uses a simple stochastic market fraction (MF) asset pricing model to investigate market dominance, profitability, and how traders adopting fundamental analysis or trend following strategies can survive under various market conditions in the long/short-run. This contrasts with the modern theory of finance which relies on the paradigm of utility maximizing representative agents and rational expectations assumptions which some contemporary theorists regard as extreme. This school of thought would predict that trend followers will be driven out of the markets in the long-run. Our analysis shows that in a MF framework this is not necessarily the case and that trend followers can survive in the long-run.
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References
Arnold, L (1998) Random Dynamical Systems. Springer-Verlag, Berlin
Arthur W, Holland J, LeBaron B, Palmer R, Tayler P (1997) Asset Pricing Under Endogeneous Expectations in an Artifical Stock Market. Economic Notes 26 (2):297-330.
Brock W, Hommes C (1997) A Rational Route to Randomness. Econometrica 65:1059-1095
Brock W, Hommes C (1998) Heterogeneous Beliefs and Routes to Chaos in a Simple Asset Pricing Model. Journal of Economic Dynamics and Control 22:1235-1274
Chan, L, Lakonishok J (2004) Value and Growth Investing: Review and Update. Financial Analysts Journal 60:71-86
Chiarella C (1992) The Dynamics of Speculative Behaviour. Annals of Operations Research 37:101-123
Chiarella C, He X (2001) Asset Pricing and Wealth Dynamics under Heterogeneous Expectations. Quantitative Finance 1:509-526
Chiarella C, He X (2002) Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model. Computational Economics 19:95-132
Chiarella C, He X (2003a) Dynamics of Beliefs and Learning under αl -processes - Heterogeneous Case. Journal of Economic Dynamics & Control 27:503-531
Chiarella C, He X (2003b) Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model with a Market Maker. Macroeconomic Dynamics 7:503-536
Cochrane J (2001) Asset Pricing. Princeton University Press, Princeton
Day R, Huang W (1990) Bulls, Bears and Market Sheep. Journal of Economic Behaviour and Organization 14:299-329
Ding Z, Granger C, Engle R (1993) A Long Memory Property of Stock market Returns and a New Model. Journal of Empirical Finance 1:83-106
Friedman M (1953) The Case for Flexible Exchange Rate. In: Essays in positive economics. Chicago, University of Chicago Press
Gaunersdorfer A (2000) Endogenous Fluctuations in a Simple Asset Pricing Model with Heterogeneous Agents. Journal of Economic Dynamics and Control 24:799-831
Haugen R (2003) The New Finance: Overreaction, Complexity and Uniqueness. Prentice Hall, Upper Saddle River, NJ
He X, Li Y (2007a) Heterogeneity, Convergence, and Autocorrelations. Quantitative Finance in press
He X, Li Y (2007b) Power-law behaviour, Heterogeneity, and Trend Chasing. Journal of Economic Dynamics and Control 31:3396-3426
Hommes C (2002) Modeling the Stylized Facts in Finance Through Simple Nonlinear Adaptive Systems, Proceedings of National Academy of Science of the United States of America, 99, 7221-7228.
Hommes C (2006) Heterogeneous Agent Models in Economics and Finance, Handbook of Computational Economics. Volume 2, Edited by K.L. Judd and L. Tesfatsion, Elsevier Science.
LeBaron B (2000) Agent-based Computational Finance: Suggested Readings and Early Research. Journal of Economic Dynamics & Control 24:679-702
LeBaron B (2001) A Builder's Guide to Agent-based Financial Markets. Quantitative Finance 1(2):254-261
LeBaron B (2002) Calibrating an Agent-based Financial Market to Macroeconomic Time Series. Technical report, Brandeis University, Waltham, MA
LeBaron B (2006) Agent-based Computational Finance. In Judd K. and Tesfatsion L. (ed) Handbook of Computational Economics Volume 2. Elsevier Science.
Levy M, Levy H, Solomon S (2000) Microscopic Simulation of Financial Markets. Academic Press, New York.
Li Y, Donkers B, and Melenberg B (2006a) Econometric Analysis of Microscopic Simulation Models. CentER Discussion Papers 2006-99, Tilburg University. Available at: http://ssrn.com/abstract=939518.
Li Y, Donkers B, and Melenberg B (2006b) The Nonparametric and Semiparametric Analysis of Microscopic Simulation Models. CentER Discussion Papers 2006-95, Tilburg University. Available at: http://ssrn.com/abstract=939510.
Lux T (1998) The Social-economic Dynamics of Speculative Markets: Interacting Agents, Chaos, and the Fat Tails of Return Distributions Journal of Economic Behaviour & Organization 33:143-165
Lux T, Marchesi M (1999) Scaling and Criticality in a Stochastic Multi-agent Model of a Financial Markets. Nature 397(11):498-500
Pagan A (1996) The Econometrics of Financial Markets. Journal of Empirical Finance 3:15-102
Shiller R (2003) From Efficient Markets Theory to Behavioural Finance. Journal of Economic Perspectives 17(1):83-104
Taylor M, Allen H (1992) The Use of Technical Analysis in the Foreign Exchange Market. Journal of International Money and Finance 11:304-314
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He, XZ., Hamill, P., Li, Y. (2008). Can Trend Followers Survive in the Long-Run% Insights from Agent-Based Modeling. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77477-8_14
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DOI: https://doi.org/10.1007/978-3-540-77477-8_14
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