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Taring all investors with the same brush? Evidence for heterogeneity in individual preferences from a maximum likelihood approach

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Listed:
  • Hackethal, Andreas
  • Jakusch, Sven Thorsten
  • Meyer, Steffen

Abstract

Microeconomic modeling of investors behavior in financial markets and its results crucially depends on assumptions about the mathematical shape of the underlying preference functions as well as their parameterizations. With the purpose to shed some light on the question, which preferences towards risky financial outcomes prevail in stock markets, we adopted and applied a maximum likelihood approach from the field of experimental economics on a randomly selected dataset of 656 private investors of a large German discount brokerage firm. According to our analysis we find evidence that the majority of these clients follow trading pattern in accordance with Prospect Theory (Kahneman and Tversky (1979)). We also find that observable sociodemographic and personal characteristics such as gender or age don't seem to correlate with specific preference types. With respect to the overall impact of preferences on trading behavior, we find a moderate impact of preferences on trading decisions of individual investors. A classification of investors according to various utility types reveals that the strength of the impact of preferences on an investors' rading behavior is not connected to most personal characteristics, but seems to be related to round-trip length.

Suggested Citation

  • Hackethal, Andreas & Jakusch, Sven Thorsten & Meyer, Steffen, 2016. "Taring all investors with the same brush? Evidence for heterogeneity in individual preferences from a maximum likelihood approach," SAFE Working Paper Series 147, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewp:147
    DOI: 10.2139/ssrn.2845866
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    More about this item

    Keywords

    Utility Theory; Maximum Likelihood; Individual Investors;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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