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

Xue-Zhong He, Philip Hamill, Youwie Li

    Research output: Chapter in Book/Report/Conference proceedingChapter

    1 Citation (Scopus)

    Abstract

    This paper uses a simple stochastic market fraction (MF) asset pric-ing model to investigate market dominance, profitability, and how traders adoptingfundamental analysis or trend following strategies can survive under various marketconditions in the long/short-run. This contrasts with the modern theory of financewhich relies on the paradigm of utility maximizing representative agents and ratio-nal expectations assumptions which some contemporary theorists regard as extreme.This school of thought would predict that trend followers will be driven out of themarkets in the long-run. Our analysis shows that in a MF framework this is notnecessarily the case and that trend followers can survive in the long-run.
    LanguageEnglish
    Title of host publicationNatural Computing in Computational Finance
    EditorsAnthony Brabazon, Michael O'Neill
    Pages253-269
    Publication statusPublished - 2008

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    Follower
    Agent-based modeling
    Paradigm
    Profitability
    Asset markets
    Modeling
    Schools of thought
    Representative agent
    Short-run
    Traders

    Cite this

    He, X-Z., Hamill, P., & Li, Y. (2008). Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling. In A. Brabazon, & M. O'Neill (Eds.), Natural Computing in Computational Finance (pp. 253-269)
    He, Xue-Zhong ; Hamill, Philip ; Li, Youwie. / Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling. Natural Computing in Computational Finance. editor / Anthony Brabazon ; Michael O'Neill. 2008. pp. 253-269
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    He, X-Z, Hamill, P & Li, Y 2008, Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling. in A Brabazon & M O'Neill (eds), Natural Computing in Computational Finance. pp. 253-269.

    Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling. / He, Xue-Zhong; Hamill, Philip; Li, Youwie.

    Natural Computing in Computational Finance. ed. / Anthony Brabazon; Michael O'Neill. 2008. p. 253-269.

    Research output: Chapter in Book/Report/Conference proceedingChapter

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    He X-Z, Hamill P, Li Y. Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling. In Brabazon A, O'Neill M, editors, Natural Computing in Computational Finance. 2008. p. 253-269