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The Evolution of Security Designs

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  • THOMAS H. NOE
  • MICHAEL J. REBELLO
  • JUN WANG

Abstract

We consider a competitive and perfect financial market in which agents have heterogeneous cash flow valuations. 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 affects security design profoundly, with securities mispriced even in the long run and optimal designs trading 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, but that otherwise produce stable payoffs, almost the exact opposite of the pure state claims that are optimal in the rational expectations framework. Copyright 2006 by The American Finance Association.

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Bibliographic Info

Article provided by American Finance Association in its journal The Journal of Finance.

Volume (Year): 61 (2006)
Issue (Month): 5 (October)
Pages: 2103-2135

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Handle: RePEc:bla:jfinan:v:61:y:2006:i:5:p:2103-2135

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References

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  1. Franklin Allen & Douglas Gale, . "Optimal Security Design," Rodney L. White Center for Financial Research Working Papers 26-87, Wharton School Rodney L. White Center for Financial Research.
  2. Hirshleifer, David, 2001. "Investor Psychology and Asset Pricing," MPRA Paper 5300, University Library of Munich, Germany.
  3. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  4. Routledge, Bryan R., 2001. "Genetic Algorithm Learning To Choose And Use Information," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 303-325, April.
  5. H. Peyton Young, 1996. "The Economics of Convention," Journal of Economic Perspectives, American Economic Association, vol. 10(2), pages 105-122, Spring.
  6. William A. Brock & Blake D. LeBaron, 1995. "A Dynamic Structural Model for Stock Return Volatility and Trading Volume," NBER Working Papers 4988, National Bureau of Economic Research, Inc.
  7. 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.
  8. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-41, June.
  9. repec:cup:macdyn:v:5:y:2001:i:2:p:303-25 is not listed on IDEAS
  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, 06.
  11. Gale, Douglas, 1992. "Standard Securities," Review of Economic Studies, Wiley Blackwell, vol. 59(4), pages 731-55, October.
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Citations

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Cited by:
  1. 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.
  2. 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.
  3. Lensberg, Terje & Schenk-Hoppé, Klaus Reiner & Ladley, Dan, 2012. "Costs and Benefits of Speculation," Discussion Papers 2012/12, Department of Business and Management Science, Norwegian School of Economics.

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