The Journal of Finance

Corporate Financing: An Artificial Agent‐based Analysis

Published: 05/06/2003   |   DOI: 10.1111/1540-6261.00554

Thomas H. Noe, Michael J. Rebello, Jun Wang

We examine corporate security choice by simulating an economy populated by adaptive agents who learn about the structure of security returns and prices through experience. Through a process of evolutionary selection, each agent gravitates toward strategies that generate the highest payoffs. Despite the fact that markets are perfect and agents maximize value, a financing hierarchy emerges in which straight debt dominates other financing choices. Equity and convertible debt display significant underpricing. In general, the smaller the probability of loss to outside investors, the more likely the firm is to issue the security and the smaller the security's underpricing.

The Evolution of Security Designs

Published: 09/19/2006   |   DOI: 10.1111/j.1540-6261.2006.01052.x

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

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.