Modelling an Initial Public Offering Using Action Theory
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Bachelor Thesis from the year 2019 in the subject Mathematics - Statistics, grade: 79, language: English, abstract: Unravel the intricate dance of valuation and strategy in the high-stakes world of Initial Public Offerings (IPOs). This incisive study delves into the heart of bidder behavior, dissecting the dynamics of auction theory as applied to IPO markets. Explore how the presence-or absence-of complete information shapes bidding strategies and ultimately influences market outcomes. The analysis begins with a rigorous examination of IPO auctions under complete information, constructing detailed models to illustrate optimal bidding, resource allocation, and the social planner's perspective. These models are expanded to accommodate varying numbers of bidders, revealing nuanced strategic interactions. The investigation then progresses to the more complex realm of incomplete information, incorporating both public and private signals that bidders use to refine their valuations. Bayesian belief revision takes center stage as the mechanism through which bidders update their understanding of the underlying asset's worth. The study meticulously examines the implications of these information asymmetries, shedding light on phenomena such as the winner's curse and the persistent first-day return anomaly often observed in IPOs. By comparing theoretical predictions with empirical data, this research offers a fresh perspective on the efficiency and fairness of IPO auctions. Furthermore, it proposes potential model extensions, including considerations of asymmetric talent among bidders and repeated game dynamics, paving the way for future exploration of this fascinating intersection of finance, economics, and behavioral science. Keywords: Initial Public Offering (IPO), Auction Theory, Bidder Behavior, Complete Information, Incomplete Information, Bayesian Belief Revision, Winner's Curse, First-Day Return Anomaly, Common Value Auction, Bidding Strategies. This book is a must-re
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