Skip to main content

Can Trend Followers Survive in the Long-Run% Insights from Agent-Based Modeling

  • Chapter
Natural Computing in Computational Finance

Part of the book series: Studies in Computational Intelligence ((SCI,volume 100))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arnold, L (1998) Random Dynamical Systems. Springer-Verlag, Berlin

    MATH  Google Scholar 

  2. 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.

    Google Scholar 

  3. Brock W, Hommes C (1997) A Rational Route to Randomness. Econometrica 65:1059-1095

    Article  MATH  MathSciNet  Google Scholar 

  4. 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

    Article  MATH  MathSciNet  Google Scholar 

  5. Chan, L, Lakonishok J (2004) Value and Growth Investing: Review and Update. Financial Analysts Journal 60:71-86

    Article  Google Scholar 

  6. Chiarella C (1992) The Dynamics of Speculative Behaviour. Annals of Operations Research 37:101-123

    Article  MATH  MathSciNet  Google Scholar 

  7. Chiarella C, He X (2001) Asset Pricing and Wealth Dynamics under Heterogeneous Expectations. Quantitative Finance 1:509-526

    Article  MathSciNet  Google Scholar 

  8. Chiarella C, He X (2002) Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model. Computational Economics 19:95-132

    Article  MATH  Google Scholar 

  9. Chiarella C, He X (2003a) Dynamics of Beliefs and Learning under αl -processes - Heterogeneous Case. Journal of Economic Dynamics & Control 27:503-531

    Article  MATH  MathSciNet  Google Scholar 

  10. 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

    MATH  MathSciNet  Google Scholar 

  11. Cochrane J (2001) Asset Pricing. Princeton University Press, Princeton

    Google Scholar 

  12. Day R, Huang W (1990) Bulls, Bears and Market Sheep. Journal of Economic Behaviour and Organization 14:299-329

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. Friedman M (1953) The Case for Flexible Exchange Rate. In: Essays in positive economics. Chicago, University of Chicago Press

    Google Scholar 

  15. Gaunersdorfer A (2000) Endogenous Fluctuations in a Simple Asset Pricing Model with Heterogeneous Agents. Journal of Economic Dynamics and Control 24:799-831

    Article  MATH  Google Scholar 

  16. Haugen R (2003) The New Finance: Overreaction, Complexity and Uniqueness. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  17. He X, Li Y (2007a) Heterogeneity, Convergence, and Autocorrelations. Quantitative Finance in press

    Google Scholar 

  18. He X, Li Y (2007b) Power-law behaviour, Heterogeneity, and Trend Chasing. Journal of Economic Dynamics and Control 31:3396-3426

    Article  MATH  MathSciNet  Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. 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.

    Google Scholar 

  21. LeBaron B (2000) Agent-based Computational Finance: Suggested Readings and Early Research. Journal of Economic Dynamics & Control 24:679-702

    Article  MATH  Google Scholar 

  22. LeBaron B (2001) A Builder's Guide to Agent-based Financial Markets. Quantitative Finance 1(2):254-261

    Article  Google Scholar 

  23. LeBaron B (2002) Calibrating an Agent-based Financial Market to Macroeconomic Time Series. Technical report, Brandeis University, Waltham, MA

    Google Scholar 

  24. LeBaron B (2006) Agent-based Computational Finance. In Judd K. and Tesfatsion L. (ed) Handbook of Computational Economics Volume 2. Elsevier Science.

    Google Scholar 

  25. Levy M, Levy H, Solomon S (2000) Microscopic Simulation of Financial Markets. Academic Press, New York.

    Google Scholar 

  26. 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.

  27. 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.

  28. 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

    Google Scholar 

  29. Lux T, Marchesi M (1999) Scaling and Criticality in a Stochastic Multi-agent Model of a Financial Markets. Nature 397(11):498-500

    Article  Google Scholar 

  30. Pagan A (1996) The Econometrics of Financial Markets. Journal of Empirical Finance 3:15-102

    Article  Google Scholar 

  31. Shiller R (2003) From Efficient Markets Theory to Behavioural Finance. Journal of Economic Perspectives 17(1):83-104

    Article  Google Scholar 

  32. Taylor M, Allen H (1992) The Use of Technical Analysis in the Foreign Exchange Market. Journal of International Money and Finance 11:304-314

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Youwei Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-77477-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77476-1

  • Online ISBN: 978-3-540-77477-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics