Concurrent Auctions Research Articles

Trustful double auction design for Peer-to-Peer energy trading between interconnected micro-grids with supply–demand imbalance

Peer-to-Peer (P2P) energy trading between Interconnected Micro-Grids (IMGs) presents a promising approach for enhancing the economic advantages for prosumers while mitigating supply–demand imbalances within individual Micro-Grids (MGs). This paper proposes a trustful double auction mechanism for P2P energy trading among prosumers in IMGs, structured into two distinct stages. Initially, concurrent auctions are conducted within each MG to facilitate intra-grid trading, followed by subsequent auctions facilitating inter-grid trading across IMGs. We figure out the allocation and pricing rules that satisfy the required properties in mechanism design even when the auction consists of two stages. Given that energy transactions across IMGs entail non-negligible power losses, equitable allocation of these losses among prosumers is paramount. To address this, we integrate a fair cost distribution methodology into our auction mechanism, implemented by an iterative algorithm. Rigorous analysis substantiates our proposed auction mechanism’s incentive compatibility, individual rationality, and budget balance, thereby fostering truthful and voluntary prosumer participation while averting potential market deficits. Numerical analysis underscores the efficacy of our approach, showcasing a significant improvement in P2P transactions and supply–demand balance enhancement compared to trading solely within individual MGs.

Secondary spectrum allocation framework via concurrent auctions for 5G and beyond networks

Secondary spectrum allocation framework via concurrent auctions for 5G and beyond networks

Short-Term Electricity Demand Forecasting Using Components Estimation Technique

Currently, in most countries, the electricity sector is liberalized, and electricity is traded in deregulated electricity markets. In these markets, electricity demand is determined the day before the physical delivery through (semi-)hourly concurrent auctions. Hence, accurate forecasts are essential for efficient and effective management of power systems. The electricity demand and prices, however, exhibit specific features, including non-constant mean and variance, calendar effects, multiple periodicities, high volatility, jumps, and so on, which complicate the forecasting problem. In this work, we compare different modeling techniques able to capture the specific dynamics of the demand time series. To this end, the electricity demand time series is divided into two major components: deterministic and stochastic. Both components are estimated using different regression and time series methods with parametric and nonparametric estimation techniques. Specifically, we use linear regression-based models (local polynomial regression models based on different types of kernel functions; tri-cubic, Gaussian, and Epanechnikov), spline function-based models (smoothing splines, regression splines), and traditional time series models (autoregressive moving average, nonparametric autoregressive, and vector autoregressive). Within the deterministic part, special attention is paid to the estimation of the yearly cycle as it was previously ignored by many authors. This work considers electricity demand data from the Nordic electricity market for the period covering 1 January 2013–31 December 2016. To assess the one-day-ahead out-of-sample forecasting accuracy, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are calculated. The results suggest that the proposed component-wise estimation method is extremely effective at forecasting electricity demand. Further, vector autoregressive modeling combined with spline function-based regression gives superior performance compared with the rest.

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.

Market Information and Bidder Heterogeneity in Secondary Market Online B2B Auctions

Online auction environments provide several sources of information that can be used by bidders to form their bids. The impact of this information on bidding is likely to be higher when ambiguously specified products are auctioned. The secondary market represents one such environment, where used and returned items from big-box retailers are auctioned off to other business buyers. Using a proprietary data set of secondary market auctions hosted on an online business-to-business platform, we study how market pricing information from other auctions for comparable products affects the bidding behavior of the first bidder. First bidders are shown to be influenced by two sets of pricing information: prices from their own historical bidding behavior and concurrent prices, formed from other open and just-finished auctions relative to the focal auction. We also study how the impact of this market pricing information is moderated by bidder heterogeneity, captured by bidder experience and cross-bidding activity on the platform across comparable concurrent auctions. Finally, we show that the first bid is significantly associated with the auction’s final price, contingent on the type of first bidder. Auctions are increasingly being used for the sale of ill-defined and idiosyncratic products; our work thus provides managerial implications for how auctioneers may design and manage the flow of relevant information to different classes of bidders on their sites in order to enhance their yields. This paper was accepted by Lorin Hitt, information systems.

Optimal Design of Two-Sided Market Platforms: An Empirical Case Study of Ebay

While much is known about optimal design of auctions within the context of a single item for sale, little is known about optimal design of large platform markets like eBay and auto auction houses that house large numbers of concurrent auctions. We attempt a macro-level empirical market design exercise by combining a unique dataset on tablet sales collected from eBay over the course of a year with methodologies developed by Bodoh-Creed (2011) and Backus and Lewis (2013). The former proposes a tractable approach to studying dynamic auction markets when the number of participants on both sides is sufficiently large. The model also delivers predictions on optimal fee schedule design -- specifically, in terms of listing fees and percentage charges for a sale -- for an auction house wishing to maximize profits by attracting the appropriate mix of buyers and sellers into the market. We begin by empirically investigating the key assumptions of the model which deliver (computational and empirical) tractability, and find that they are reasonable. We then estimate consumer demand, market supply, and the distributions of market entrants (this part still in progress). These figures are plugged into the Bodoh-Creed (2011) framework in order to compute optimal fee schedules and draw comparisons to actual fee schedules, as well as to make policy prescriptions.

Reference Prices and Bidder Heterogeneity in Secondary Market Online B2B Auctions

Online auction environments provide several sources of information that can be used by bidders to form their bids. One such information set that has been relatively understudied in the literature pertains to reference prices available to the bidder from other concurrent and comparable auctions. In this paper, we study how reference prices from such auctions affect bidding behavior on the focal auction. We also study how the impact of these reference prices is moderated by bidder heterogeneity. Bidders are shown to be influenced by two sets of references prices: internal reference prices from their own historical bidding behavior and external reference prices, formed from other open and just-finished auctions relative to the focal auction. We measure bidder heterogeneity using bidder experience and level of participation in concurrent auctions. Our results show that external reference prices are significantly moderated by bidder heterogeneity. In a departure from current work, we use longitudinal data on auctions and bids in the B2B secondary markets, where goods represent salvage or returned items from big-box retailers and bidders are business buyers. The dataset comprises over 4000 auctions collected from a large liquidator firm in North America and is unique in its comprehensiveness. Our work provides theoretical insights that are complementary to the current set of results from B2C auctions as well as managerial implications for auctioneers in the B2B space.

Reference Prices and Bidder Heterogeneity in Secondary Market Online B2B Auctions

Online auction environments provide several sources of information that can be used by bidders to form their bids. The impact of this information on bidding is likely to be higher when ambiguously specifi ed products are auctioned. The secondary market represents one such environment, where used and returned items from big-box retailers are auctioned off to other business buyers. Using a proprietary dataset of secondary market auctions hosted on an online B2B platform, we study how market pricing information from other auctions for comparable products a ffects the bidding behavior of the first bidder. First bidders are shown to be influenced by two sets of pricing information: prices from their own historical bidding behavior and concurrent prices, formed from other open and just-finished auctions relative to the focal auction. We also study how the impact of these market pricing information are moderated by bidder heterogeneity, captured by bidder experience and cross bidding activity on the platform across comparable concurrent auctions. Finally, we show the fi rst bid is signi cantly associated with the auction's nal price, contingent on the type of first bidder. Auctions are increasingly being used for the sale of ill-defi ned and idiosyncratic products; our work thus provides managerial implications for how auctioneers may design and manage the flow of relevant information to di erent classes of bidders on their sites in order to enhance their yields.

Determinants of Elapsed Time to Switch between Auctions: Findings from a Hazard Function Model with Unobserved Heterogeneity

Purpose – Online auctions, which have become an important aspect of online sales, are generally regarded as stand-alone events. However, in contrast to offline auctions, online auctions can be subject to the presence of simultaneous competing auctions. This study models and estimates determinants of elapsed time to switch across concurrent auctions, with special attention to unobserved heterogeneity among bidders. Design/methodology/approach – Since auctions are dynamic and since the current winning bid progresses over time, we study time dependency over the course of an auction with hazard function models. To account for unobserved heterogeneity, we use a latent class approach, which identifies bidder segments based on both observed and unobserved factors. Findings – Our findings show significant heterogeneity across bidders, revealed by their varying degrees of propensity to switch across auctions. The three segments of bidders are The Inerts - about 30%, The Switchers - less than 10%, and The In-Betweens. According to our findings, bidders can induce other bidders to switch to a concurrent auction by responding quickly to the current high bid. Moreover, we find a surprisingly high degree of inertia and reluctance to switch towards the end of the auction when bidding is most critical. Originality/value – To our knowledge, this study is the first to model elapsed time to switch from one auction to a simultaneous auction for an identical product, and to investigate determinants of the time required to switch, with special attention to unobserved heterogeneity across bidders.

Determinants of elapsed time to switch between auctions

PurposeOnline auctions, which have become an important aspect of online sales, are generally regarded as stand‐alone events. However, in contrast to offline auctions, online auctions can be subject to the presence of simultaneous competing auctions. The purpose of this study is to model and estimate determinants of elapsed time to switch across concurrent auctions, with special attention to unobserved heterogeneity among bidders.Design/methodology/approachSince auctions are dynamic and since the current winning bid progresses over time, the authors study time dependency over the course of an auction with hazard function models. To account for unobserved heterogeneity, the paper uses a latent class approach, which identifies bidder segments based on both observed and unobserved factors.FindingsThe findings show significant heterogeneity across bidders, revealed by their varying degrees of propensity to switch across auctions. The three segments of bidders are The Inerts – about 30 percent, The Switchers – less than 10 percent, and The In‐Betweens. According to the findings, bidders can induce other bidders to switch to a concurrent auction by responding quickly to the current high bid. Moreover, the paper finds a surprisingly high degree of inertia and reluctance to switch towards the end of the auction when bidding is most critical.Originality/valueTo the authors' knowledge, this study is the first to model elapsed time to switch from one auction to a simultaneous auction for an identical product, and to investigate determinants of the time required to switch, with special attention to unobserved heterogeneity across bidders.

Profiling Online Auction Sellers Using Image-Editing Styles

Product images serve an important role in online auction listings. As thriving businesses, online auction sites often host millions of concurrent auction listings. Where space is limited (such as on the page of auction search results), only product images are displayed to users as an overview of all auction listings. To stand out from competitors, veteran sellers often edit product images to attract potential buyers. Over time, many sellers have developed their own editing styles that recurrently appear in their image pool and are mostly distinct from other sellers, indicating a promising feature for seller profiling. Seller profiling is fundamental for the detection of account anomalies, which are often related to fraudulent acts. Numerous online auction guides suggest that buyers watch for anomalies in a seller's auction listings (such as sudden changes in product categories, auction templates, and text fonts), because such anomalies often indicate account takeovers. Researchers have proposed computational methods to encode such features and automate the detection of anomalies and frauds. However, little previous work has leveraged product images, a major component of auction listings. We developed an automatic algorithm that can extract image editing styles to establish seller profiles.

Sniping and Squatting in Auction Markets

We conducted a field experiment to test the benefit from late bidding (sniping) in online auction markets. We compared sniping to early bidding (squatting) in auctions for newly-released DVDs on eBay. Sniping led to a statistically significant increase in our average surplus. However, this improvement was small. The two bidding strategies resulted in a variety of other qualitative differences in the outcomes of auctions. We show that a model of multiple concurrent auctions, in which our opponents are naïve or incremental bidders as identified in the lab, explain the results well. Our findings illustrate how the overall impact of naïveté, and the benefit from sniping observed in the lab, may be substantially attenuated in real-world market settings. (JEL D44)

Modelling Concurrency of Events in On-Line Auctions via Spatiotemporal Semiparametric Models

Summary We introduce a semiparametric approach for modelling the effect of concurrent events on an outcome of interest. Concurrency manifests itself as temporal and spatial dependences. By temporal dependence we mean the effect of an event in the past. Modelling this effect is challenging since events arrive at irregularly spaced time intervals. For the spatial part we use an abstract notion of ‘feature space’ to conceptualize distances among a set of item features. We motivate our model in the context of on-line auctions by modelling the effect of concurrent auctions on an auction's price. Our concurrency model consists of three components: a transaction-related component that accounts for auction design and bidding competition, a spatial component that takes into account similarity between item features and a temporal component that accounts for recently closed auctions. To construct each of these we borrow ideas from spatial and mixed model methodology. The power of this model is illustrated on a large and diverse set of laptop auctions on eBay.com. We show that our model results in superior predictive performance compared with a set of competitor models. The model also allows for new insight into the factors that drive price in on-line auctions and their relationship to bidding competition, auction design, product variety and temporal learning effects.

Modeling Concurrency of Events in Online Auctions via Spatio-Temporal Semiparametric Models

We introduce a semiparametric approach for modeling the effect of concurrent events on an outcome of interest. Concurrency manifests itself as temporal and spatial dependencies. By temporal dependency we mean the effect of an event in the past. Modeling this effect is challenging since events arrive at irregularly spaced time intervals and thus the standard definition of time-lags does not apply. Our concurrency model also takes spatial effects into account. We interpret the meaning of in a slightly non-traditional way in order to conceptualize the more abstract notion of space among a set of item-features. We motivate our model in the context of eBay's online auctions. In particular, we model the effect of concurrent auctions on an auction's price. Our concurrency model consists of three components: a transaction-related component that accounts for auction-design and bidding-competition, a spatial component that takes into account similarity among item-features, and a temporal component that accounts for events in the past. To construct each of these model components, we borrow ideas from spatial modeling and from the mixed model methodology. We also develop a new time-lag metric to handle unevenly-spaced time series by interpreting a time-lag as the information distributed over a certain time-window in the past and by incorporating this information via inclusion of appropriate summary measures. We illustrate the power of this model by applying it to a large and diverse set of laptop auctions crawled off 1 eBay.com. We show that our model results in superior predictive performance compared to a set of competitor models. Our model also allows for new insight into the factors that drive price in eBay's online auctions and their relationship to bidding-competition, auction-design, product-variety and temporal learning effects.

Visualizing Online Auctions

Online auctions have been the subject of many empirical research efforts in the fields of economics and information systems. These research efforts are often based on analyzing data from Web sites such as eBay.com which provide public information about sequences of bids in closed auctions, typically in the form of tables on HTML pages. The existing literature on online auctions focuses on tools like summary statistics and more formal statistical methods such as regression models. However, there is a clear void in this growing body of literature in developing appropriate visualization tools. This is quite surprising, given that the sheer amount of data that can be found on sites such as eBay.com is overwhelming and can often not be displayed informatively using standard statistical graphics. In this article we introduce graphical methods for visualizing online auction data in ways that are informative and relevant to the types of research questions that are of interest. We start by using profile plots that reveal aspects of an auction such as bid values, bidding intensity, and bidder strategies. We then introduce the concept of statistical zooming (STAT-zoom) which can scale up to be used for visualizing large amounts of auctions. STAT-zoom adds the capability of looking at data summaries at various time scales interactively. Finally, we develop auction calendars and auction scene visualizations for viewing a set of many concurrent auctions. The different visualization methods are demonstrated using data on multiple auctions collected from eBay.com.

The effects of shilling on final bid prices in online auctions

An increasing number of reports of online auction fraud are of growing concern to auction operators and participants. In this research, we discuss reserve price shilling, where a bidder shills in order to avoid paying auction house fees, rather than to drive up the price of the final bid. We examine the effect that premium bids have upon the final selling price, since they are linked with reserve price shill bids. We use 10,260 eBay auctions during April 2001, and identify 919 auctions involving 322 sellers and 1583 bidders involved in concurrent auctions for the exact same item. We find that premium bidding occurs 23% of the time, in 263 of the 919 auctions. Using a theoretical perspective involving valuation signals, we show that other bidders may view high bids as signals that an item is worth more. Thus, they may be willing to pay more for the items than others that do not receive premium bids. The implications are disturbing in that sellers may be more motivated to enter a shill bid in order to drive up the final price in an online auction. We also examine and report on alternative hypotheses involving winner’s curse and the possibility of reserve price shill bids. Our results are developed in the context of a weighted least squares regression model that predicts an item selling price-to- average selling price ratio.

Concurrent auctions across the supply chain

With the recent technological feasibility of electronic commerce over the Internet, much attention has been given to the design of electronic markets for various types of electronically-tradable go...

Concurrent Auctions Across The Supply Chain

With the recent technological feasibility of electronic commerce over the Internet, much attention has been given to the design of electronic markets for various types of electronically-tradable goods. Such markets, however, will normally need to function in some relationship with markets for other related goods, usually those downstream or upstream in the supply chain. Thus, for example, an electronic market for rubber tires for trucks will likely need to be strongly influenced by the rubber market as well as by the truck market. In this paper we design protocols for exchange of information between a sequence of markets along a single supply chain. These protocols allow each of these markets to function separately, while the information exchanged ensures efficient global behavior across the supply chain. Each market that forms a link in the supply chain operates as a double auction, where the bids on one side of the double auction come from bidders in the corresponding segment of the industry, and the bids on the other side are synthetically generated by the protocol to express the combined information from all other links in the chain. The double auctions in each of the markets can be of several types, and we study several variants of incentive compatible double auctions, comparing them in terms of their efficiency and of the market revenue.

An Efficient Concurrent Auction Model For Firm Natural Gas Transportation Capacity

In accordance with the reform policy of the Federal Energy Regulatory Commission, open access to natural gas pipeline networks should allow large customers an opportunity to compete for cost-effective natural gas supplies. The competitive criterion is that all potential customers have equivalent access at the market clearing price to any building blocks in natural gas transportation service. Access to firm transportation capacity rights, defined as reliability priority blocks at critical bottlenecks, is a workable mechanism for planning and managing capacity allocations on the pipeline network. A concurrent auction optimization model provides an allocation of and prices for capacity rights for a given period, with allocations reflecting the willingness to pay.

An Example of a Multi-Object Auction Game

Concurrent auctions of several objects are traditionally analyzed as if they were independent single-object auctions. Such an approximation may be very crude if bidders have budgetary restrictions, capacity constraints, or, in general, have non-linear utility functions. This paper presents a very simple multi-object auction for which explicit equilibrium strategies can be calculated; these equilibrium strategies have several qualitative characteristics arising from the multi-object nature of the example and therefore not present in typical single-object auctions. In particular, these results suggest that the observed variance in bids for offshore oil leases is not solely due to bidder uncertainty, but also to the method of auction currently employed.