Modeling first bid in retail secondary market online auctions: A Bayesian approach. (29th December 2019)

• Record Type: Journal Article
• Authors: Zafari, Babak; Soyer, Refik
• Is Part Of: Applied stochastic models in business and industry. Volume 36:Number 3(2020)
• Page Start: 452
• Page End: 464
• Publication Date: 2019-12-29
• Subjects: beta regression -- bidder behavior -- B2B auction -- dynamic probit; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2498
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract: We propose a Bayesian framework to model bid placement time in retail secondary market online business‐to‐business auctions. In doing so, we propose a Bayesian beta regression model to predict the first bidder and time to first bid, and a dynamic probit model to analyze participation. In our development, we consider both auction‐specific and bidder‐specific explanatory variables. While we primarily focus on the predictive performance of the models, we also discuss how auction features and bidders' heterogeneity could affect the bid timings, as well as auction participation. We illustrate the implementation of our models by applying to actual auction data and discuss additional insights provided by the Bayesian approach, which can benefit auctioneers.