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Estimating the Intensity of Choice in a Dynamic Mutual Fund Allocation Decision

Author

Listed:
  • David Goldbaum

    (Rutgers University - Newark)

  • Bruce Mizrach

    (Rutgers University)

Abstract

We estimate the intensity of choice parameter in heterogenous agent models in both a static and dynamic setting. Mean-variance optimizing agents choose among mutual funds of similar styles but varying performance. Actively managed funds have a lower Sharpe ratio than passive index funds, yet they attract a majority share of asset allocation. By estimating the relative growth of passive funds, we obtain a dynamic estimate of the intensity of choice calibrated to 10 years of mutual fund flows.

Suggested Citation

  • David Goldbaum & Bruce Mizrach, 2004. "Estimating the Intensity of Choice in a Dynamic Mutual Fund Allocation Decision," Departmental Working Papers 200414, Rutgers University, Department of Economics.
    • Handle: RePEc:rut:rutres:200414
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    Cited by:

    1. Palczewski, Jan & Schenk-Hoppé, Klaus Reiner & Wang, Tongya, 2016. "Itchy feet vs cool heads: Flow of funds in an agent-based financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 63(C), pages 53-68.
    2. Anufriev, Mikhail & Bao, Te & Tuinstra, Jan, 2016. "Microfoundations for switching behavior in heterogeneous agent models: An experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 129(C), pages 74-99.
    3. Jang, Tae-Seok & Sacht, Stephen, 2012. "Identification of animal spirits in a bounded rationality model: An application to the euro area," Economics Working Papers 2012-12, Christian-Albrechts-University of Kiel, Department of Economics.
    4. Xue-Zhong He & Youwei Li, 2017. "The adaptiveness in stock markets: testing the stylized facts in the DAX 30," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1071-1094, November.
    5. Michael Wegener & Frank Westerhoff, 2012. "Evolutionary competition between prediction rules and the emergence of business cycles within Metzler’s inventory model," Journal of Evolutionary Economics, Springer, vol. 22(2), pages 251-273, April.
    6. Tae-Seok Jang & Stephen Sacht, 2016. "Animal Spirits and the Business Cycle: Empirical Evidence from Moment Matching," Metroeconomica, Wiley Blackwell, vol. 67(1), pages 76-113, February.
    7. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    8. De Grauwe, Paul & Markiewicz, Agnieszka, 2013. "Learning to forecast the exchange rate: Two competing approaches," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 42-76.
    9. Lux, Thomas, 2012. "Estimation of an agent-based model of investor sentiment formation in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1284-1302.
    10. Chiarella, Carl & He, Xue-Zhong & Huang, Weihong & Zheng, Huanhuan, 2012. "Estimating behavioural heterogeneity under regime switching," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 446-460.
    11. Mahayni, Antje & Schoenmakers, John G.M., 2011. "Minimum return guarantees with fund switching rights—An optimal stopping problem," Journal of Economic Dynamics and Control, Elsevier, vol. 35(11), pages 1880-1897.
    12. Cars Hommes, 2010. "The heterogeneous expectations hypothesis: some evidence from the lab," Post-Print hal-00753041, HAL.
    13. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    14. Chernulich, Aleksei, 2021. "Modelling reference dependence for repeated choices: A horse race between models of normalisation," Journal of Economic Psychology, Elsevier, vol. 87(C).
    15. Zheng, Min & Liu, Ruipeng & Li, Youwei, 2018. "Long memory in financial markets: A heterogeneous agent model perspective," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 38-51.
    16. Goldbaum, David & Zwinkels, Remco C.J., 2014. "An empirical examination of heterogeneity and switching in foreign exchange markets," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 667-684.
    17. Blake LeBaron, 2011. "Active and Passive Learning in Agent-based Financial Markets," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 37(1), pages 35-43.
    18. Lines Marji & Westerhoff Frank, 2012. "Effects of Inflation Expectations on Macroeconomic Dynamics: Extrapolative Versus Regressive Expectations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-30, October.
    19. Anufriev, M. & Tuinstra, J. & Bao, T., 2013. "Fund Choice Behavior and Estimation of Switching Models: An Experiment," CeNDEF Working Papers 13-04, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    20. Anufriev, Mikhail & Chernulich, Aleksei & Tuinstra, Jan, 2018. "A laboratory experiment on the heuristic switching model," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 21-42.

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    More about this item

    Keywords

    heterogenous agents; intensity of choice; mutual funds;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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