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Can Myopic Loss Aversion Explain the Equity Premium Puzzle? Evidence from a Natural Field Experiment with Professional Traders

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  • Francis Larson
  • John List
  • Robert Metcalfe

Abstract

Behavioral economists have recently put forth a theoretical explanation for the equity premium puzzle based on combining myopia and loss aversion. Complementing the behavioral theory is evidence from laboratory experiments, which provide strong empirical support consistent with myopic loss aversion (MLA). Yet, whether, and to what extent, such preferences underlie behaviors of traders in their natural domain remains unknown. Indeed, a necessary condition for the MLA theory to explain the equity premium puzzle is for marginal traders in markets to exhibit such preferences. Using minute-by-minute trading observations from over 864,000 price realizations in a natural field experiment, we find data patterns consonant with MLA: in their normal course of business, professional traders who receive infrequent price information invest 33% more in risky assets, yielding profits that are 53% higher, compared to traders who receive frequent price information. Beyond testing theory, these results have important implications for efficient resource allocation as well as characterizing the optimal structure of social and economic policies.

Suggested Citation

  • Francis Larson & John List & Robert Metcalfe, 2016. "Can Myopic Loss Aversion Explain the Equity Premium Puzzle? Evidence from a Natural Field Experiment with Professional Traders," Natural Field Experiments 00534, The Field Experiments Website.
  • Handle: RePEc:feb:natura:00534
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    Cited by:

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    2. Kazi Iqbal & Asad Islam & John List & Vy Nguyen, 2021. "Myopic Loss Aversion and Investment Decisions: From the Laboratory to the Field," Framed Field Experiments 000730, The Field Experiments Website.
    3. Trutmann, Kevin & Heinke, Steve & Rieskamp, Jörg, 2023. "Take your time: How delayed information and restricted decision opportunities improve belief formation in investment decisions," Finance Research Letters, Elsevier, vol. 51(C).
    4. Adam Farago & Martin Holmén & Felix Holzmeister & Michael Kirchler & Michael Razen, 2022. "Cognitive Skills and Economic Preferences in the Fund Industry," The Economic Journal, Royal Economic Society, vol. 132(645), pages 1737-1764.
    5. Kazuo Sano, 2022. "New Concept for the Value Function of Prospect Theory," Papers 2211.00131, arXiv.org, revised Jun 2024.
    6. Iturbe-Ormaetxe, Iñigo & Ponti, Giovanni & Tomás, Josefa, 2019. "Is it myopia or loss aversion? A study on investment game experiments," Economics Letters, Elsevier, vol. 180(C), pages 36-40.
    7. Johannes Carow & Niklas M. Witzig, 2024. "Time Pressure and Strategic Risk-Taking in Professional Chess," Working Papers 2404, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    8. Mosenhauer, Moritz, 2020. "Information Management against Excessive Stock Trading: More or Less? Or Both?," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224549, Verein für Socialpolitik / German Economic Association.
    9. Constantin Mellios & Anh Ngoc Lai, 2022. "Incentive Fees with a Moving Benchmark and Portfolio Selection under Loss Aversion," Post-Print hal-03708926, HAL.
    10. Penghang Liu & Kshama Dwarakanath & Svitlana S Vyetrenko & Tucker Balch, 2022. "Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets," Papers 2210.08569, arXiv.org, revised Mar 2024.
    11. Hueber, Laura & Schwaiger, Rene, 2022. "Debiasing through experience sampling: The case of myopic loss aversion," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 87-138.
    12. Morais, Marcleiton Ribeiro & Schoti, Camila & Resende, José Guilherme de Lara & Tabak, Benjamin Miranda, 2023. "Limits to Myopic loss aversion and learning," Economics Letters, Elsevier, vol. 229(C).
    13. Nicholas Barberis & Lawrence J. Jin & Baolian Wang, 2021. "Prospect Theory and Stock Market Anomalies," Journal of Finance, American Finance Association, vol. 76(5), pages 2639-2687, October.

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    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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