Measuring short-term risk of initial public offering of equity securities: a hybrid Bayesian and Data-Envelopment-Analysis-based approach

• Published in: Annals of operations research 2020-05, Vol.288 (2), p.733-753
• Main Authors: Sorkhi, Shabnam, Paradi, Joseph C.
• Format: Article
• Language: English
• Subjects: Analysis; Bayesian analysis; Bayesian statistical decision theory; Business and Management; Combinatorics; Data analysis; Data envelopment analysis; Going public (Securities); Initial public offerings; Iterative methods; Market risk; Measurement; Methodology; Operations research; Operations Research/Decision Theory; Prices and rates; Probability density functions; Risk analysis; S.I.: Data Envelopment Analysis: Four Decades On; Short term; Statistical inference; Stocks; Theory of Computation; Uncertainty; Uniqueness
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-019-03439-0

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.