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Computational Complexity and Information Asymmetry in Financial Products

Author

Listed:
  • Sanjeev Arora

    (Princeton University)

  • Boaz Barak

    (Princeton University)

  • Markus Brunnermeier

    (Princeton University)

  • Rong Ge

    (Princeton University)

Abstract

Traditional economics argues that financial derivatives, like CDOs and CDSs, ameliorate the negative costs imposed by asymmetric information. This is because securitization via derivatives allows the informed party to find buyers for less information-sensitive part of the cash flow stream of an asset (e.g., a mortgage) and retain the remainder. In this paper we show that this viewpoint may need to be revised once computational complexity is brought into the picture. Using methods from theoretical computer science this paper shows that derivatives can actually amplify the costs of asymmetric information instead of reducing them. Note that computational complexity is only a small departure from full rationality since even highly sophisticated investors are boundedly rational due to a lack of requisite computational resources.

Suggested Citation

  • Sanjeev Arora & Boaz Barak & Markus Brunnermeier & Rong Ge, 2009. "Computational Complexity and Information Asymmetry in Financial Products," Working Papers 2009-1, Princeton University. Economics Department..
  • Handle: RePEc:pri:econom:2009-1
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    File URL: https://scholar.princeton.edu/sites/default/files/derivative_0.pdf
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    References listed on IDEAS

    as
    1. Bernardo, Antonio E & Cornell, Bradford, 1997. "The Valuation of Complex Derivatives by Major Investment Firms: Empirical Evidence," Journal of Finance, American Finance Association, vol. 52(2), pages 785-798, June.
    2. Gerd Gigerenzer & Reinhard Selten (ed.), 2002. "Bounded Rationality: The Adaptive Toolbox," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262571641, December.
    3. Joshua Coval & Jakub Jurek & Erik Stafford, 2009. "The Economics of Structured Finance," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 3-25, Winter.
    4. Markus K. Brunnermeier, 2008. "Deciphering the Liquidity and Credit Crunch 2007-08," NBER Working Papers 14612, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Colliard, Jean-Edouard & Georg, Co-Pierre, 2025. "Measuring regulatory complexity," Journal of Financial Economics, Elsevier, vol. 174(C).
    2. Xing Gao & Daniel Ladley, 2022. "Noise trading and market stability," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1283-1301, October.

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    Keywords

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    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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