Corporate Financing: An Artificial Agent-based Analysis
Thomas H. Noe
University of Oxford - Said Business School; University of Oxford - Balliol College; European Corporate Governance Institute
Michael J. Rebello
University of Texas at Dallas - Naveen Jindal School of Management
Zicklin School of Business, Baruch College
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. Each agent experiments with different strategies and, through a process of evolutionary selection, gravitates toward strategies that generate the highest payoffs. Despite the fact that markets are perfect and agents maximize value, financing decisions are relevant because of pricing errors generated by imperfect learning. A financing hierarchy emerges in which straight debt dominates other financing choices. Equity and convertible debt securities display significant underpricing. Complex securities with theoretical appeal are not issued frequently. In general, the safety-first criterion defines the hierarchy of security issuance, that is, the smaller the probability of loss to outside investors produced by a security, the more likely the firm is to issue the security and the smaller the security's underpricing.
Number of Pages in PDF File: 44
Keywords: corporate financing, adaptive learning, genetic algorithm, security choice
JEL Classification: C63, D83, G321working papers series
Date posted: March 3, 2002
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