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The Evolution of Security Designs

Author

Listed:
  • Noe, Thomas H.

    (A.B. Freeman School of Business)

  • Rebello, Michael J.

    (A.B. Freeman School of Business)

  • Wang, Jun

    (Baruch College)

Abstract

This paper embeds security design in a model of evolutionary learning. We consider a competitive and perfect financial market where agents, as in Allen and Gale (1988), have heterogeneous valuations for cash flows. Our point of departure is that, instead of assuming that agents are endowed with rational expectations, we model their behavior as the product of adaptive learning. Our results demonstrate that adaptive learning profoundly affects security design. Securities are mispriced even in the long run and optional designs trade off underpricing against intrinsic value maximization. The evolutionary dominant security design calls for issuing securities that engender large losses with a small but positive probability, and otherwise produce stable payoffs. These designs are almost the exact opposite of the pure state claims which are optimal in the rational expectations framework but are roughly consistent with what one would expect given the decision making heuristics documented in the behavioural economics literature.

Suggested Citation

  • Noe, Thomas H. & Rebello, Michael J. & Wang, Jun, 2004. "The Evolution of Security Designs," SIFR Research Report Series 26, Institute for Financial Research.
  • Handle: RePEc:hhs:sifrwp:0026
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    References listed on IDEAS

    as
    1. Brock, William A & LeBaron, Blake D, 1996. "A Dynamic Structural Model for Stock Return Volatility and Trading Volume," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 94-110, February.
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    3. Franklin Allen, Douglas Gale, 1988. "Optimal Security Design," Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 229-263.
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    8. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    9. Douglas Gale, 1992. "Standard Securities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 59(4), pages 731-755.
    10. Thomas H. Noe & Michael J. Rebello & Jun Wang, 2003. "Corporate Financing: An Artificial Agent‐based Analysis," Journal of Finance, American Finance Association, vol. 58(3), pages 943-973, June.
    11. H. Peyton Young, 1996. "The Economics of Convention," Journal of Economic Perspectives, American Economic Association, vol. 10(2), pages 105-122, Spring.
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    Citations

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    Cited by:

    1. Xinyang Li & Andreas Krause, 2011. "An evolutionary multi‐objective optimization of trading rules in call markets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(1), pages 1-14, January.
    2. Benjamin E. Hermalin & Michael S. Weisbach, 2012. "Information Disclosure and Corporate Governance," Journal of Finance, American Finance Association, vol. 67(1), pages 195-234, February.
    3. Lensberg, Terje & Schenk-Hoppé, Klaus Reiner & Ladley, Dan, 2015. "Costs and benefits of financial regulation: Short-selling bans and transaction taxes," Journal of Banking & Finance, Elsevier, vol. 51(C), pages 103-118.
    4. Ladley, Daniel & Lensberg, Terje & Palczewski, Jan & Schenk-Hoppé, Klaus Reiner, 2015. "Fragmentation and stability of markets," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 466-481.
    5. Qixuan Luo & Yu Shi & Xuan Zhou & Handong Li, 2021. "Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1025-1049, December.
    6. Rydqvist, Kristian, 2010. "Tax Arbitrage with Risk and Effort Aversion - Swedish Lottery Bonds 1970-1990," SIFR Research Report Series 70, Institute for Financial Research.
    7. Lensberg, Terje & Schenk-Hoppé, Klaus Reiner & Ladley, Dan, 2012. "Costs and Benefits of Speculation," Discussion Papers 2012/12, Norwegian School of Economics, Department of Business and Management Science.
    8. Dreber, Anna & Rand, David G. & Garcia, Justin R. & Wernerfelt, Nils & Lum, J. Koji & Zeckhauser, Richard, 2010. "Dopamine and Risk Preferences in Different Domains," Working Paper Series rwp10-012, Harvard University, John F. Kennedy School of Government.
    9. Noe, Thomas H. & Rebello, Michael & Wang, Jun, 2012. "Learning to bid: The design of auctions under uncertainty and adaptation," Games and Economic Behavior, Elsevier, vol. 74(2), pages 620-636.
    10. Lijian Wei & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2013. "Learning and Information Dissemination in Limit Order Markets," Research Paper Series 333, Quantitative Finance Research Centre, University of Technology, Sydney.

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    More about this item

    Keywords

    Corporate financing; Adaptive learning; Genetic algorithm; Security choice;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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