The evolution of security designs

• 成果类型: Article
• 署名作者: Noe, Thomas H.; Rebello, Michael J.; Wang, Jun
• 署名单位: Tulane University; Louisiana State University System; Louisiana State University
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/j.1540-6261.2006.01052.x
• 发表日期: 2006
• 页码: 2103-2135
• 关键词: genetic algorithm

摘要

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.