IPO mechanism selection by using Classification and Regression Trees

IPO mechanism selection by using Classification and Regression Trees The Turkish IPO market gives issuers and underwriters a choice of three different IPO selling mechanisms. The current paper sheds new light on the determinants of these issue procedures within the context of the following methods (i) book building mechanism, (ii) fixed price offer, and (iii) sale through the stock exchange. Most of the empirical models in the IPO literature use binary probit and logit models to determine the factors behind the choice of one method over another and try to answer the question of “why is such a mechanism chosen”. To understand the reasons on issuers’ selection of IPO mechanism, we have conducted a Classification and Regression Trees (CART) methodology to represent decision rules in a form of binary trees. Our results indicate that, CART methodology predicts a firms’ IPO selling mechanism with 77.42% accuracy. The most important variable that determines the IPO selling mechanism is the Arrangement Type between the issuer and the underwriter as in the form of best effort and firm-commitment. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

IPO mechanism selection by using Classification and Regression Trees

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Publisher
Springer Netherlands
Copyright
Copyright © 2011 by Springer Science+Business Media B.V.
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-011-9430-4
Publisher site
See Article on Publisher Site

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