Price In Online Auctions Research Articles

A Comprehensive Exploration of Machine Learning Models for Predicting Online Auction Prices

The transformative impact of traditional commerce by online marketplaces is exemplified through eBay, a global platform that facilitates diverse transactions via auctions. In this research, the dynamics of eBay auctions, crucial for buyers, sellers, and researchers, are delved into. The central inquiry revolves around the key factors shaping auction outcomes, examining bid behaviors and types. The study leverages a robust dataset from eBay, meticulously curated to encompass auction identifiers, bid details, pricing information, auction types, and temporal aspects. A comprehensive approach involves data preprocessing, ensuring reliability by addressing missing values and outliers. Rigorous exploration and validation validate the dataset's integrity. Machine Learning Techniques, including MLP, SVR, Linear Regression, Extra Trees, and Gradient Boosting, form the analytical backbone. Model evaluation reveals top-performing candidates, such as MLP Regressor (0.8084), SVR (0.8210), and Linear Regression (0.8173), exhibiting superior accuracy and reliability. These models are identified for adoption in future work, emphasizing nuanced predictions in eBay auctions. This research contributes to understanding online auction dynamics, offering practical insights for eBay users and the broader e-commerce community. The models identified pave the way for enhanced predictive capabilities and continuous refinement in deciphering factors influencing auction outcomes.

A Comprehensive Exploration of Machine Learning Models for Predicting Online Auction Prices

The transformative impact of traditional commerce by online marketplaces is exemplified through eBay, a global platform that facilitates diverse transactions via auctions. In this research, the dynamics of eBay auctions, crucial for buyers, sellers, and researchers, are delved into. The central inquiry revolves around the key factors shaping auction outcomes, examining bid behaviors and types. The study leverages a robust dataset from eBay, meticulously curated to encompass auction identifiers, bid details, pricing information, auction types, and temporal aspects. A comprehensive approach involves data preprocessing, ensuring reliability by addressing missing values and outliers. Rigorous exploration and validation validate the dataset's integrity. Machine Learning Techniques, including MLP, SVR, Linear Regression, Extra Trees, and Gradient Boosting, form the analytical backbone. Model evaluation reveals top-performing candidates, such as MLP Regressor (0.8084), SVR (0.8210), and Linear Regression (0.8173), exhibiting superior accuracy and reliability. These models are identified for adoption in future work, emphasizing nuanced predictions in eBay auctions. This research contributes to understanding online auction dynamics, offering practical insights for eBay users and the broader e-commerce community. The models identified pave the way for enhanced predictive capabilities and continuous refinement in deciphering factors influencing auction outcomes.

Are female skins sold at a lower price? Evidence from the Fortnite game

ABSTRACT Much significant research has been done to shed light on discrimination of females in, for example, labour markets. Less is known, in contrast, about the amount of discrimination in the virtual world of online gaming. In an early study, Castronova (2004), using data for the EverQuest game, finds that female avatars receive about 10% lower prices in online auctions. In this research note, we re-examine the pricing of avatars using data for the Fortnite game and find small and statistically insignificant price difference between female and male avatars.

A Novel Auction Blockchain System with Price Recommendation and Trusted Execution Environment

Online auctions are now widely used, with all the convenience and efficiency brought by internet technology. Despite the advantages over traditional auction methods, some challenges still remain in online auctions. According to the World Business Environment Survey (WBES) conducted by the World Bank, about 60% of companies have admitted to bribery and manipulation of the auction results. In addition, buyers are prone to the winner’s curse in an online auction environment. Since the increase in information availability can reduce uncertainty, easy access to relevant auction information is essential for buyers to avoid the winner’s curse. In this study, we propose an Online Auction Price Suggestion System (OAPSS) to protect the data from being interfered with by third-party programs based on Intel’s Software Guard Extensions (SGX) technology and the characteristics of the blockchain. Our proposed system provides a smart contract by using α-Sutte indicator in the final transaction price prediction as a bidding price recommendation, which helps buyers to reduce the information uncertainty on the value of the product. The amount spent on the smart contract in this study, excluding deployed contracts, plus the rest of the fees is less than US$1. Experimental results of the simulation show that there is a significant difference (p < 0.05) between the recommended price group and the actual price group in the highest bid. Therefore, we may conclude that our proposed bidder’s price recommendation function in the smart contract may mitigate the loss of buyers caused by the winner’s curse.

The effect of surcharge on price in online auctions

The effect of surcharge on price in online auctions

Empirical Analyses of Nonlinear Effects of Reserve Prices on Ending Prices in Online Auctions

Empirical results about the effect of open reserve price on ending prices in auctions are mixed, with some researchers finding a positive effect on ending price and others reporting a negative or no effect. The objective of this research is to propose a new theoretical framework. First, without an open reserve price, auctions attract more bidders, resulting in increased competition and higher ending prices—a so-called competition effect. Second, a high open reserve price may serve as a price floor or a reference price, influencing bidders' valuations and increasing ending prices.Quantile regression is used, which has the advantage of providing robust parameter estimates of the effect of open reserve prices on ending prices for different levels of the reserve price. Estimates of a longitudinal data set for four products collected over a period of one year show an u-shaped relationship between open reserve prices and ending prices, where either no reserves or high reserves results in higher ending prices. By comparing ending prices of auctions with an open reserve versus a secret reserve, it is shown that open reserve prices have a reference-price effect rather than a price-floor effect.

Modeling eBay price using stochastic differential equations

AbstractOnline auctions have become increasingly popular in recent years. There is a growing body of research on this topic, whereas modeling online auction price curves constitutes one of the most interesting problems. Most research treats price curves as deterministic functions, which ignores the random effects of external and internal factors. To account for the randomness, a more realistic model using stochastic differential equations is proposed in this paper. The online auction price is modeled by a stochastic differential equation in which the deterministic part is equivalent to the second‐order differential equation model proposed in Wang et al. (Journal of the American Statistical Association, 2008, 103, 1100–1118). The model also includes a component representing the measurement errors. Explicit expressions for the likelihood function are also obtained, from which statistical inference can be conducted. Forecast accuracy of the proposed model is compared with the ODE (ordinary differential equation) approach. Simulation results show that the proposed model performs better.

The effects of competitive reserve prices in online auctions

PurposeThis paper aims to investigate how competition among online auction sellers influences the setting of both open and secret reserve prices, thereby affecting auction outcome.Design/methodology/approachUsing a data set collected from eBay consisting of 787 identical product auctions, three empirical models have been proposed. Model 1 simultaneously estimates the effects of auction competition on a seller’s own open and secret reserve price strategies; Model 2 estimates the effects of auction competition on bidder participation; and Model 3 estimates the direct and indirect effects of auction competition on selling price.FindingsCompetition among sellers is central to shaping sellers’ reserve price strategies. When there are more concurrent auctions for identical items, sellers tend to specify a low open reserve and are less likely to set a secret reserve. Sellers are strongly influenced by competitors’ reserve price strategies, and tend to follow competition. Finally, auction competition and competitive reserve price strategies influence both bidder entry and selling prices.Practical implicationsThis study has important implications for both sellers and bidders. It highlights the importance for sellers to adapt their reserve price strategies in light of their competitors’ reserve price strategies and offers implications for bidders regarding auction selection. An auction with low starting bid does not necessarily lead to a lower selling price as it attracts more bidders.Originality/valueThis paper focuses on competition among auction sellers, whereas previous literature has focused on competition among bidders. This paper is the first to study the impact of competing reserve prices in auctions.

Signaling, Bidding and the Final Price in the Online Auction Platform Almost Without Information Asymmetry: An Empirical Study on China's Largest B2C Marketplace

Business-to-customer (B2C) online auctions differ from customer-to-customer (C2C) online auctions by having much less sellers who have established their reputations. Thus, B2C online auctions face much lower levels of information asymmetry and relies more on seller’s signaling and buyers’ bidding to determine the final price. This paper uses the second-hand product auction data from Jingdong, the largest B2C electronic market in China, to understand the determinants of the final price in B2C online auctions. We find that the signaling from the single seller and the bidding from the buyers can almost fully explain the final price, where the signaling is much more important than the demand side’s bidding, and posting the price of new product selling in the online market is the most effective signaling. These findings are robust after controlling the endogeneity of the number of bidders. Results from Quantile IV Regression further show that the valuation-reference role of the seller’s signaling such as posting the price of new product and the newness levels of the product decreases with the value of product price while the role of bidding behaviors such as the number and the heterogeneity of bidders increases with that value.

Modeling and Forecasting Online Auction Prices: A Semiparametric Regression Analysis

Interest in online auctions has been growing in recent years. There is an extensive literature on this topic, whereas modeling online auction price process constitutes one of the most active research areas. Most of the research, however, only focuses on modeling price curves, ignoring the bidding process. In this paper, a semiparametric regression model is proposed to model the online auction process. This model captures two main features of online auction data: changing arrival rates of bidding processes and changing dynamics of prices. A new inference procedure using B‐splines is also established for parameter estimation. The proposed model is used to forecast the price of an online auction. The advantage of this proposed approach is that the price can be forecast dynamically and the prediction can be updated according to newly arriving information. The model is applied to Xbox data with satisfactory forecasting properties. Copyright © 2016 John Wiley & Sons, Ltd.

Reserve Prices in Online Auctions:Theory and Evidence

The paper proposes a simple model of auctions with an impatient seller who chooses the reserve price and the buy-it-now (BIN) price to maximize revenue. The three main sales channels in the online auction (the pure auction, the BIN auction, and the fixed-price sale) are each shown to be an optimal solution for the sellers with certain degrees of time-preference. The theory also predicts that the optimal posted price for the fixed-price listing is greater than the optimal reserve price for the BIN auction, which in turn is greater than that for the pure auction. This prediction is confirmed by data from eBay's auction of iPods. Using the seller's inventory as a proxy for his degree of time-preference, the data also confirm the theoretical prediction that there is an inverse relationship between the optimal reserve price and the seller's time-discount factor.

Combating Shill Bidding in Real Time: Prevention, Detection and Response

Human cheating has been a barrier to establishing trust among e-commerce users, throughout the last two decades. In particular, in online auctions, since all the transactions occur among anonymous users, trust is difficult to establish and maintain. Shill bidding happens when bidders bid exclusively to inflate (in forward auctions) or deflate (in reverse auctions) prices in online auctions. At present, shill bidding is the most severe and persistent form of cheating in online auctions, but still there are only a few or no established techniques for shill defense at run-time. In this paper, I evaluate the strengths and weaknesses of existing approaches to combating shill bidding. I also propose the ShillFree1 auction system to secure and protect auction systems from shill bidders for both forward and reverse auctions. More precisely, by using a variety of bidding behavior and user history, proposed auction system prevents, monitors and detects shill activities in real time. Moreover, to detect shilling thoroughly I propose IP tracking techniques. The system also takes necessary actions against shill activities at run-time. The experimental results demonstrate that, by prevention, detection and response mechanisms, the proposed auction system keeps the auction users secured from shill bidding and therefore establishes trust among online auction users.

Forecasting Online Auctions via Self‐Exciting Point Processes

ABSTRACTModeling online auction prices is a popular research topic among statisticians and marketing analysts. Recent research mainly focuses on two directions: one is the functional data analysis (FDA) approach, in which the price–time relationship is modeled by a smooth curve, and the other is the point process approach, which directly models the arrival process of bidders and bids. In this paper, a novel model for the bid arrival process using a self‐exciting point process (SEPP) is proposed and applied to forecast auction prices. The FDA and point process approaches are linked together by using functional data analysis technique to describe the intensity of the bid arrival point process. Using the SEPP to model the bid arrival process, many stylized facts in online auction data can be captured. We also develop a simulation‐based forecasting procedure using the estimated SEPP intensity and historical bidding increment. In particular, prediction interval for the terminal price of merchandise can be constructed. Applications to eBay auction data of Harry Potter books and Microsoft Xbox show that the SEPP model provides more accurate and more informative forecasting results than traditional methods. Copyright © 2014 John Wiley & Sons, Ltd.

A semiparametric varying coefficient model of monotone auction bidding processes

We propose an econometric model of the price processes of online auctions. Since auction bids monotonically increase within individual auctions but can differ considerably across auctions, we construct a monotone series estimator with a common relative price growth curve and auction-specific slopes. Furthermore, because the impacts of auction-specific attributes may evolve along the course of auctions, we employ a varying coefficient approach to accommodate these possibly time-varying effects. We present an algorithm to solve the proposed partially linear panel model with a nonparametric monotone component. We apply the proposed model to eBay auctions of a Palm personal digital assistant. The estimates capture closely the overall pattern of online auction price processes, in particular, the bidding drought midway through auctions and the bid acceleration associated with bid sniping (last-minute bidding) at the end of auctions.

Linear Probability Models (LPM) and Big Data: The Good, the Bad, and the Ugly

Sizes of datasets used in academic research are growing quickly, with many studies using tens and hundreds of thousands or even millions of records. Linear regression is among the most popular statistical model in social sciences research. Linear probability models, which are linear regression models applied to a binary outcome, are commonly used for various reasons, despite criticisms of such usage. We carry out an extensive study to evaluate the use of LPMs in the realm of Big Data, where large samples and many variables are available. We evaluate performance in terms of coefficient estimation as well as predictive power. We compare performance to alternatives suggested in the literature. We find that the LPM is beneficial for descriptive modeling when the outcome is naturally binary, whereas it is beneficial for predictive modeling when the outcome is binary by discretization. We motivate and illustrate our study through an application to modeling price in online auctions, using real data from the online auction site eBay.

The determinants of price in online auctions: more evidence from unbalanced panel data

This study provides an alternative approach that takes account of the unobserved effects of each seller under a sample selection framework while using online auction data. We use data collected from Yahoo! Kimo Auction (Taiwan) to demonstrate that earlier empirical results of online auction studies may be biased due to violating the assumption of independence of the error terms between sample observations. Empirical findings show that seller reputation is no longer as the most important factor for buyers to bid on items, while the sample data confirm the unobserved heterogeneity of sellers and sample selection problem.

Bid pricing in online auctions with "buy-it-now" option

The present note suggests a theoretic and practical decision rule for auctioneers to set out prices in online auctions in presence of Buy-It-Now options. The minimum starting bid and the Buy-It-Now price are calculated as an intuitive function of seller’s evaluation of the final price stochastic distribution and of seller’s attitude toward the price variability. Specifically, the presence of personalized parameters permits to incorporate the seller pessimism in stating the starting minimum bid and the seller optimism in fixing the Buy-It-Now price. The pricing rule is based on the Extended Gini premium principle, a well-known premium principle in non-life insurance literature that has been recently proposed in different areas of finance and portfolio risk management. In addition to give closed-end formulae for the starting minimum bid and Buy-It-Now price, we provide numerical tables for a number of common distributions for the ending price at different levels of seller’s risk-aversion and gain-propensity. Discussion on the spread between the starting minimum bid and Buy-It-Now price is also carried out.

PRICING IN ONLINE AUCTION PROCUREMENT A REVIEW OF EMPIRICAL METHODS AND CURRENT UNDERSTANDINGS

The online auction has become an important channel for procurement and sourcing management. As firms often expect lower procurement prices through online auctions, how the prices are determined in online auctions should be of major interest to procurement managers and supply chain researchers. Despite the abundant empirical studies on online auction prices, an aggregated view is still absent. This study fills this gap with a review of extant studies. More specifically, this study provides summaries of all major theories behind online auction pricing, defines and analyzes often encountered econometric issues, and discusses how the treatments of these issues have been operationalized. Towards the end, existing findings on determinants of online auction prices are integrated and examined. The purpose of this study is to provide a convenient and precise package of current studies for researchers and professionals.

Empirical dynamics for longitudinal data

We demonstrate that the processes underlying on-line auction price bids and many other longitudinal data can be represented by an empirical first order stochastic ordinary differential equation with time-varying coefficients and a smooth drift process. This equation may be empirically obtained from longitudinal observations for a sample of subjects and does not presuppose specific knowledge of the underlying processes. For the nonparametric estimation of the components of the differential equation, it suffices to have available sparsely observed longitudinal measurements which may be noisy and are generated by underlying smooth random trajectories for each subject or experimental unit in the sample. The drift process that drives the equation determines how closely individual process trajectories follow a deterministic approximation of the differential equation. We provide estimates for trajectories and especially the variance function of the drift process. At each fixed time point, the proposed empirical dynamic model implies a decomposition of the derivative of the process underlying the longitudinal data into a component explained by a linear component determined by a varying coefficient function dynamic equation and an orthogonal complement that corresponds to the drift process. An enhanced perturbation result enables us to obtain improved asymptotic convergence rates for eigenfunction derivative estimation and consistency for the varying coefficient function and the components of the drift process. We illustrate the differential equation with an application to the dynamics of on-line auction data.

Seller Search and Market Outcomes in Online Auctions

It has become widely accepted that information technology (IT) has had a profound influence on market transactions, with lower search costs for buyers often considered the key to lower prices and higher consumer welfare. However, sellers also use online channels to search for buyers whose product preferences and valua-tions match the sellers’ offerings, and limited research has examined the impact of IT on sellers’ search strat-egies or associated market outcomes. In this study, we investigate how market characteristics, the length of time the seller searches the auction marketplace for a buyer, and seller search in other websites influence the final sale price in online auctions. The online auction market for used vehicles provides a valuable oppor-tunity to observe search processes of sellers, as the unique VIN associated with each vehicle enables the tracking of an individual item across both subsequent auctions and across other websites. We find that the market characteristics, duration of search, and search in other websites are each associated with a higher final sale price. Our findings highlight the importance of examining both buyer and seller search behaviors to fur-ther our understanding of the impacts of IT on market outcomes and efficiency.