Measuring short-term risk of initial public offering of equity securities: a hybrid Bayesian and Data-Envelopment-Analys
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Measuring short-term risk of initial public offering of equity securities: a hybrid Bayesian and Data-Envelopment-Analysis-based approach Shabnam Sorkhi1
· Joseph C. Paradi1
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract This paper offers a methodology to estimate an unconditional probability density function (PDF) for the stock price of an initial public offering (IPO), at a short-term post-IPO horizon. The resultant PDF is unique to the IPO of interest (IPOI) and serves to model the short-term post-market uncertainty associated with its price. Such a methodology is unprecedented in the IPO risk literature since the ex ante quantification of the short-term uncertainty associated with the stock price of a newly public firm was viewed as burdened by the lack of sufficient accounting and market history at the IPO stage. This gap is addressed here through recognizing that common in most IPO cases are the scarcity of hard data and abundance of soft data (strong prior belief), and that one can combine Bayesian inference and Data Envelopment Analysis (DEA) to develop a unique risk quantification setting that befits and serves these two characteristics of IPOs. In this setting, DEA serves to quantify the prior belief, to be subsequently updated in the Bayesian phase. This paper remains the first of its kind which unravels the IPO risk analysis from such perspective. It develops an iterative process that uses DEA to design a multi-dimensional similarity metric to find the ‘comparables’ to IPOI, and thereof the closest comparable to it, whereupon Bayesian inference is employed to utilize the information available from these comparables to sequentially update and revise the IPOI’s prior PDF. The validity of the proposed risk methodology was examined by backtesting analyses. Keywords Data Envelopment Analysis · Initial public offerings · Bayesian · Financial risk · Investment decision processes
The authors would like to note that a summary of the methodology presented in this paper has been published as a book chapter in Paradi et al. (2018): “Data Envelopment Analysis in the Financial Services Industry: A Guide for Practitioners and Analysts Working in Operations Research Using DEA.” The methodology comprises two main phases, referred to as Phase I (Sect. 3.1 in this paper) and Phase II (Sect. 3.2). The published chapter in the book brought more clarity to Phase I, de-scoping a detailed illustration of Phase II, whose full particulars are given here and is viewed as the authors’ primary contribution. With regard to the chronological sequence of the events, the initial intention was to have the current paper published in advance of the 2018 edition of the book. However, due to the lengthy review process, the paper was delayed but the book appeared in print much earlier than expected, and the current paper had to be cited as a working paper in the book. The authors aim to update the citation in any future revision of the book. Extended author information available on the last page of the arti
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