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

Xue-Zhong He, Philip Hamill, Youwei Li

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    This paper 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.
    LanguageEnglish
    Title of host publicationNatural Computing in Computational Finance
    EditorsAnthony Brabazon, Michael O'Neill
    Pages253-269
    Publication statusPublished - 2008

    Fingerprint

    Agent-based modeling
    Follower
    Paradigm
    Profitability
    Schools of thought
    Finance
    Short-run
    Fundamental analysis
    Rational expectations
    Traders
    Asset pricing models
    Market conditions
    Representative agent

    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, Youwei. / 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, Youwei.

    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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    BT - Natural Computing in Computational Finance

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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