The Evolution of Security Designs
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.
|Date of creation:||15 Sep 2004|
|Date of revision:|
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- 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.
- 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.
- 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.
- 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.
- Routledge, Bryan R., 2001. "Genetic Algorithm Learning To Choose And Use Information," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 303-325, April.
- repec:cup:macdyn:v:5:y:2001:i:2:p:303-25 is not listed on IDEAS
- H. Peyton Young, 1996. "The Economics of Convention," Journal of Economic Perspectives, American Economic Association, vol. 10(2), pages 105-122, Spring.
- Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
- Hirshleifer, David, 2001.
"Investor Psychology and Asset Pricing,"
5300, University Library of Munich, Germany.
- Douglas Gale, 1992. "Standard Securities," Review of Economic Studies, Oxford University Press, vol. 59(4), pages 731-755.
- 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.
- 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.
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