Estimating Time Required to Reach Bid Levels in Online Auctions.
ABSTRACT Sellers in eBay are often small-business owners whose livelihood depends on fast turnaround of their cash flows. Unlike in traditional auctions, these sellers are content to sell as soon as some target price is reached. While a wealth of literature exists on the final rent of the various stakeholders in a traditional auction setting, what is of interest here is to estimate the time required to reach a certain bid level in ongoing auctions. This paper introduces an analytical model to estimate the time it takes an online auction to reach a pre-specified price threshold. The motivation for the research is to avoid unnecessary delays in conducting the transaction. Specifying the right duration would benefit small sellers who would realize the revenue proceeds from the sale faster. To this end, we model the bidding process as an infinite quasi-birth-death process, characterized by bursts of rapid bidding and subsequent lulls. We obtain closed-form solutions for the transient probability distribution in the frequency domain of the bid prices in an ongoing auction, which are then used to compute the transient probability distributions in the time domain. Experienced auctioneers can use these results to estimate expected ending times for their auctions. Sample observations from online auctions indicate that there might be potential room for improvement for sellers in setting their auction ending times. Simulations of the QBD processes back up the theoretical observations.
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ABSTRACT: The scenario of established business sellers utilizing online auction markets to reach consumers and sell new products is becoming increasingly common. We propose a class of risk management tools, loosely based on the concept of financial options that can be employed by such sellers. While conceptually similar to options in financial markets, we empirically demonstrate that option instruments within auction markets cannot be developed employing similar methodologies, because the fundamental tenets of extant option pricing models do not hold within online auction markets. We provide a framework to analyze the value proposition of options to potential sellers, option-holder behavior implications on auction processes, and seller strategies to write and price options that maximize potential revenues. We then develop an approach that enables a seller to assess the demand for options under different option price and volume scenarios. We compare option prices derived from our approach with those derived from the Black-Scholes model (Black & Scholes, 1973) and discuss the implications of the price differences. Experiments based on actual auction data suggest that options can provide significant benefits under a variety of option-holder behavioral patterns.Decision Sciences 07/2005; 36(3):397 - 425. · 1.36 Impact Factor
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ABSTRACT: This paper describes opportunities and challenges of using functional data analysis (FDA) for the exploration and analysis of data originating from electronic commerce (eCommerce). We discuss the special data structures that arise in the online environment and why FDA is a natural approach for representing and analyzing such data. The paper reviews several FDA methods and motivates their usefulness in eCommerce research by providing a glimpse into new domain insights that they allow. We argue that the wedding of eCommerce with FDA leads to innovations both in statistical methodology, due to the challenges and complications that arise in eCommerce data, and in online research, by being able to ask (and subsequently answer) new research questions that classical statistical methods are not able to address, and also by expanding on research questions beyond the ones traditionally asked in the offline environment. We describe several applications originating from online transactions which are new to the statistics literature, and point out statistical challenges accompanied by some solutions. We also discuss some promising future directions for joint research efforts between researchers in eCommerce and statistics.Statistical Science 10/2006; · 2.24 Impact Factor
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ABSTRACT: I study a budget-constrained, private-valuation, sealed-bid sequential auction with two incompletely-informed, risk-neutral bidders in which the valuations and income may be non-monotonic functions of a bidder's type. Multiple equilibrium symmetric bidding functions may exist that differ in allocation, efficiency and revenue. The sequence of sale affects the competition for a good and therefore also affects revenue and the prices of each good in a systematic way that depends on the relationship among the valuations and incomes of bidders. The sequence of sale may affect prices and revenue even when the number of bidders is large relative to the number of goods. If a particular good, say [alpha], is allocated to a strong bidder independent of the sequence of sale, then auction revenue and the price of good [alpha] are higher when good [alpha] is sold first.American Economic Review 01/2002; 92(4):1093-1103. · 2.69 Impact Factor