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Investment Under Uncertainty: Testing the Options Model with Professional Traders

Author

Listed:
  • John A. List

    (University of Chicago and NBER)

  • Michael S. Haigh

    (Commodities Future Trading and Commission)

Abstract

An important class of investment decisions is characterized by unrecoverable sunk costs, resolution of uncertainty through time, and the ability to invest in the future as an alternative to investing today. The options model provides guidance in such settings, including an investment decision rule called the bad news principle: the downside investment state influences the investment decision, whereas the upside investment state is ignored. This study takes a new approach to examining predictions of the options model by using the tools of experimental economics. Our evidence, drawn from student and professional trader subject pools, is broadly consonant with the options model. (c) 2010 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • John A. List & Michael S. Haigh, 2010. "Investment Under Uncertainty: Testing the Options Model with Professional Traders," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 974-984, November.
  • Handle: RePEc:tpr:restat:v:92:y:2010:i:4:p:974-984
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    1. Omar Al-Ubaydli & John A. List, 2016. "Field Experiments in Markets," NBER Working Papers 22113, National Bureau of Economic Research, Inc.
    2. Romain Gauriot Author e-mail: romain.gauriot@nyu.edu & Lionel Page Author e-mail: lionel.page@uts.edu.au, 2021. "How Market Prices React to Information: Evidence from Binary Options Markets," Working Papers 20200058, New York University Abu Dhabi, Department of Social Science, revised Oct 2021.
    3. Al-Ubaydli, Omar & Boettke, Peter, 2010. "Markets as economizers of information: Field experimental examination of the “Hayek Hypothesis”," MPRA Paper 27660, University Library of Munich, Germany.
    4. Fahr, René & Janssen, Elmar & Sureth, Caren, 2014. "Can tax rate increases foster investment under entry and exit flexibility? Insights from an economic experiment," arqus Discussion Papers in Quantitative Tax Research 166, arqus - Arbeitskreis Quantitative Steuerlehre.
    5. Alexander Puetz & Stefan Ruenzi, 2011. "Overconfidence Among Professional Investors: Evidence from Mutual Fund Managers," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 38(5-6), pages 684-712, June.
    6. Marco Angrisani & Marco Cipriani & Antonio Guarino, 2022. "Strategic Sophistication and Trading Profits: An Experiment with Professional Traders," Staff Reports 1044, Federal Reserve Bank of New York.
    7. Christoph Huber & Christian König-Kersting, 2022. "Experimenting with Financial Professionals," Working Papers 2022-07, Faculty of Economics and Statistics, Universität Innsbruck.
    8. Ryan Kellogg, 2014. "The Effect of Uncertainty on Investment: Evidence from Texas Oil Drilling," American Economic Review, American Economic Association, vol. 104(6), pages 1698-1734, June.
    9. Gong, Binglin & Lei, Vivian & Pan, Deng, 2013. "Before and after: The impact of a real bubble crash on investors’ trading behavior in the lab," Journal of Economic Behavior & Organization, Elsevier, vol. 95(C), pages 186-196.
    10. Sheedy, Elizabeth & Zhang, Le & Tam, Kenny Chi Ho, 2019. "Incentives and culture in risk compliance," Journal of Banking & Finance, Elsevier, vol. 107(C), pages 1-1.
    11. Brian Albrecht & Omar Al-Ubaydli & Peter Boettke, 2022. "Testing the Hayek hypothesis: Recent theoretical and experimental evidence," Artefactual Field Experiments 00759, The Field Experiments Website.
    12. Marco Cipriani & Roberta De Filippis & Antonio Guarino & Ryan Kendall, 2020. "Trading by Professional Traders: An Experiment," Staff Reports 939, Federal Reserve Bank of New York.
    13. Jackson Dorsey, 2019. "Waiting for the Courts: Effects of Policy Uncertainty on Pollution and Investment," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(4), pages 1453-1496, December.
    14. Dillon Bowen, 2022. "Simple models predict behavior at least as well as behavioral scientists," Papers 2208.01167, arXiv.org.
    15. Wenqing Zhang & Prasad Padmanabhan & Chia-Hsing Huang, 2015. "Sequential capital investment decision making under extreme cash fl ow situations: evidence using Monte Carlo simulation," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 16(5), pages 877-900, October.
    16. Elizabeth Sheedy & Le Zhang & Dominik Steffan, 2022. "Scorecards, gateways and rankings: remuneration and conduct in financial services," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(3), pages 3239-3283, September.
    17. Driouchi, Tarik & Trigeorgis, Lenos & So, Raymond H.Y., 2020. "Individual antecedents of real options appraisal: The role of national culture and ambiguity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1018-1032.

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    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles

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