Buyer's Bid Research Articles

Price discovery using a double auction

We investigate equilibrium in the buyer's bid double auction (BBDA) in a model with correlated private values/costs. Using a combination of theorems and computed examples, we demonstrate that simple equilibria exist even in small markets. Moreover, we bound traders' strategic behavior as a function of market size and derive rates of convergence to zero of (i) inefficiency in the allocation caused by strategic behavior and (ii) the error in the market price as an estimate of the rational expectations price. These rates together with computed examples suggest that strategic behavior can be inconsequential even in small markets in its effect on allocational efficiency and information aggregation. The BBDA thus simultaneously accomplishes both the informational and allocational goals that markets ideally fulfill; it does this perfectly in large markets and approximately in small markets, with the error due mainly to the smallness itself and not the strategic behavior of traders.

The Microstructure of the U.S. Housing Market: Evidence from Millions of Bargaining Interactions

We study the microstructure of the U.S. housing market using a novel data set comprising housing search and bargaining behavior for millions of interactions between sellers and buyers. We first establish a number of stylized facts, the most prominent being a nearly 50--50 split between houses that sold below final listing price and those that sold above final listing price. Second, we compare observed behavior with predictions from a large theoretical housing literature. Many predictions on the relationship between sales price, time on the market, listing price and atypicality are borne out in the data. However, existing models do not adequately explain the spread of the sales price around the final listing price. Using a modeling strategy that treats listing price changes as revisions of expectations about the sales price, we find sellers under-react to information shocks in estimating the sales price. Last, we find that the bargaining outcomes are influenced by previously undocumented buyers' bid characteristics, e.g., financing contingencies and escalation clauses, that signal a buyer's ability to complete or expedite the transaction. This suggests an important role for buyer bid characteristics, which are not explained by existing theories, in affecting bargaining power and surplus allocation in bilateral bargaining in housing transactions.

Coordination of Purchasing and Bidding Activities Across Markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. Most of the existing research related to trading agents assumes that there exists a combinatorial market mechanism in which buyers (or sellers) can bid (or sell) service or merchant bundles. Today's prevailing e-commerce practice, however, does not support this assumption in general and thus limits the practical applicability of these approaches. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-price markets and various online auctions that sell single items. It uses trading agents to coordinate a buyer's purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-price markets with fixed transaction costs. Flat shipping costs, a common e-tailing practice, are captured in these models. We observe that making optimal purchasing decisions in this context is NP-hard in the strong sense and suggest several efficient computational methods based on discrete location theory. The second set of models is concerned with the coordination of bidding activities across multiple online auctions. We study the underlying coordination problem for a collection of first or second-price sealed-bid auctions and derive the optimal coordination and bidding policies.

Impact of Load Profile on Dynamic Interactions Between Energy Markets: A Case Study of Power Exchange and Demand Response Exchange

Demand response exchange (DRX) presents a pool-based approach for optimal clearing/scheduling of demand response (DR) services through bid clearing. The same is intended to minimize the limitations of price-based and incentive-based mechanisms. However, unlike the price-based or incentive-based DR schemes, the DR sellers in DRX do not have a clear motivation to formulate the bid offers. Similarly, the DR buyers’ bid formulation should also include optimal benefit analysis. This paper formulates and analyzes the bid formulation for DR sellers and buyers considering respective operation objectives. The DR seller bid formulation is derived considering load behavior through utilization index and availability index obtained from load profile of respective loads. On the other hand, DR buyer bid formulation is derived from power exchange operation attributes/cost of generation. Therefore, the DR clearing ultimately affects the operational cost of operation which, in turn, is reflected in DR buying bid and DR clearing finally. This paper develops an iterative approach in which the continuous interactions between ISO/power exchange would finally converge to a stable operation. The load profile-based strategic bid formulation is modeled using mathematical as well as fuzzy inference system (FIS). In addition, an adaptive FIS has been devised to improve the performance of DR scheduling. The simulation results illustrate the impact of customer behavior, intelligent decision-making, and penetration level on the performance and convergence of power markets (DRX and power exchange).

An adaptive step-size spectrum auction mechanism for two-tier heterogeneous networks

Recently, with the rapid growth of demands for wireless communications, dynamic spectrum allocation is one of the key technologies in cognitive radio networks to resolve the realistic problem of low utilization efficiency of spectrum. It mainly focuses on how the spectrum owner dynamically allocates idle spectrum to secondary users who have no licensed spectrum for communications. In this paper, a dynamic spectrum allocation model based on auction theory in a two-tier heterogeneous network is proposed, in which the primary users (PUs) are the sellers, the central processor (CP) auctioneer is the coordinator, and femtocell base station (FBS) as the buyer bids for the idle spectrum and act as a wireless access point that provides communication services for secondary users (SUs). Its basic process is as follows: the auctioneer gradually raises the spectrum price from the reserved price; each bidder decides whether participates in the purchase or not. It is characterized by distributed execution and low complexity which can reduce unnecessary information exchange between primary users or secondary users. Meanwhile it can enhance the utilization of spectrum and improve the efficiency of the auction by generate the incentive mechanism.

Dynamic Mechanisms with Martingale Utilities

We study the dynamic mechanism design problem of a seller who repeatedly sells independent items to a buyer with private values. In this setting, the seller could potentially extract the entire buyer surplus by running efficient auctions and charging an upfront participation fee at the beginning of the horizon. In some markets, such as internet advertising, participation fees are not practical since buyers expect to inspect items before purchasing them. This motivates us to study the design of dynamic mechanisms under successively more stringent requirements that capture the implicit business constraints of these markets. We first consider a periodic individual rationality constraint, which limits the mechanism to charge at most the buyer's value in each period. While this prevents large upfront participation fees, the seller can still design mechanisms that spread a participation fee across the first few auctions. These mechanisms have the unappealing feature that they provide close-to-zero buyer utility in the first auctions in exchange for higher utility in future auctions. To address this problem, we introduce a martingale utility constraint, which imposes the requirement that from the perspective of the buyer, the next item's expected utility is equal to the present one's. Our main result is providing a dynamic auction satisfying martingale utility and periodic individual rationality whose profit loss with respect to first-best (full extraction of buyer surplus) is optimal up to polylogarithmic factors. The proposed mechanism is a dynamic two-tier auction with a hard floor and a soft floor that allocates the item whenever the buyer's bid is above the hard floor and charges the minimum of the bid and the soft floor.

LOTUS: Location-Aware Online Truthful Double Auction for Dynamic Spectrum Access

In the spectrum auction, if a buyer locates in a “critical” place, interfering with a lot of other buyers, his occupancy of the spectrum may deprive many other transmission opportunities. In this paper, we propose a Location-aware Online Truthful doUble auction Scheme (LOTUS), which incorporates the buyers' location information into auction mechanism design. In the online auction, the biggest challenge is how to allocate the spectrums based on the knowledge in the current time slot, without knowing the spectrum requests that may come afterward. To solve this problem, we propose considering the opportunity cost of allocating the spectrum to a buyer based on his local interference conditions. We introduce the “interference discount” to markdown a buyer's bid if he induces a wide range of interference. Furthermore, we take into account the spectrum heterogeneity and design mechanisms that guarantee the economic robustness of the auction. The simulation results show that LOTUS outperforms the existing online auction mechanism, significantly improving buyers' and sellers' utility.

A dynamic hybrid buyer bidding strategy for decentralized and cloud-based many-to-many negotiation

The decentralized many-to-many negotiation for resource allocation in Cloud and multi-agent systems presents numerous challenges, including ones related to the buyer strategy which is the focus of the present paper. Current approaches deriving required resources each bid must ask for aren't in all market cases an optimal choice. For this reason, we have proposed a hybrid negotiation strategy consisting of a combination of two modes of negotiation strategies that generates required resources of each bid in parallel, the first one is an existent fixed negotiation strategy and the second one is a learning selection strategy over the buyer's agreement space. Moreover, acting dynamically in the market place by adjusting appropriately the buyer's resource provisioning times and calling for proposal to hand over contracted resources in order to break some deadlocks involving buyers' tasks has been shown via simulation results to achieve better performances both in terms of social welfare and buyer utility.

The Asymptotics of Price and Strategy in the Buyer's Bid Double Auction

The analysis of equilibrium in double auctions is complicated, with almost no closed-form examples and with formal results limited to bounds on bidding behavior. We consider the buyer's bid double auction in a correlated, private values model. Sellers have the incentive to submit their costs as their asks while buyers strategically underbid. We determine (i) the asymptotic distribution of the equilibrium market price and (ii) the asymptotic limits of terms in the first order condition for a buyer's selection of his bid. Part (i) reveals the properties of the strategically determined market price as an estimator of the rational expectations equilibrium price. Part (ii) provides a simple formula for a buyer's bid that is shown numerically to closely approximate his equilibrium bid even in relatively small markets. This formula reveals how equilibrium varies with the numbers of buyers and sellers and the distribution of their values/costs.

Price Discovery

We investigate equilibrium in the buyer's bid double auction (BBDA) in a model with correlated signals and either private or interdependent values. Using a combination of theorems and numerical experiments, we demonstrate that simple equilibria exist even in small markets. Moreover, we bound traders' strategic behavior as a function of market size and derive rates of convergence to zero of (i) inefficiency in the allocation caused by strategic behavior and (ii) the error in the market price as an estimate of the rational expectations price. These rates together with numerical experiments suggest that strategic behavior is inconsequential even in small markets in its effect on allocational efficiency and information aggregation. The BBDA thus simultaneously accomplishes both the informational and allocational goals that markets ideally fulfill; it does this perfectly in large markets and approximately in small markets, with the error attributable mainly to the smallness itself and not the strategic behavior of traders.

Optimality versus practicality in market design: A comparison of two double auctions

We consider a market for indivisible items with m buyers and m sellers. Traders privately know their values/costs, which are statistically dependent. Two mechanisms are considered. The buyer's bid double auction collects bids and asks from traders and determines the allocation by selecting a market-clearing price. It fails to achieve all possible gains from trade because of strategic bidding. The designed mechanism is a revelation mechanism in which honest reporting of values/costs is incentive compatible and all gains from trade are achieved. This optimality, however, comes at the expense of plausibility: (i) the monetary transfers among the traders are defined in terms of the traders' beliefs about each other's value/cost; (ii) a trader may suffer a loss ex post; (iii) the mechanism may run a surplus/deficit ex post. We compare the virtues of the simple yet mildly inefficient buyer's bid double auction to the flawed yet perfectly efficient designed mechanism.

Photograph and model use within an online auction page for influencing buyer's bidding behavior

PurposeThis paper concentrates on the visual information within a product description block in an online auction page, which is the only place a seller can manipulate to influence bidders. The purpose of this paper is to examine the effects of photograph use, model use, and the physical attractiveness of the model on bidding behavior.Design/methodology/approachA mock online auction website was built to manipulate picture use, model use, and the physical attractiveness of the model. The experiment recruited volunteers and randomly assigned each participant into one of four treatments. After experiencing the stimulus for two minutes, the researchers asked participants to evaluate their bidding intention and write down the highest price they would bid.FindingsThe results show that using a photograph to introduce the product acquires a higher bidding intention and bidding amount than not using a photograph. In addition, a web page using physically attractive models in photographs acquires a higher bidding intention and bidding price than a web page using physically unattractive models in photographs.Originality/valueThis study is unique in discussing the effect of the visual content of the product description block on online bidding behavior. The results of this study provide suggestions and a product information presentation strategy for online auction sellers.

List price and sale price variation across the housing market cycle

PurposeThe purpose of this paper is to explore how the difference between the sale price and list price of houses varies across the market cycle.Design/methodology/approachThe paper utilises quarterly transaction‐based information on house prices from the Belfast Metropolitan Area. The information is structured on a time series basis from 2002 to 2008. The analysis is concerned with the mean differences between list price and sale price, the standard deviation of the differences, the skewness and kurtosis of the distributions.FindingsThe results show that under normal market conditions the mean deviation between list price and sale price is small circa 1 per cent. However, the departure between list price and sale price becomes substantial on both the up‐ and down‐cycles of the market. The analysis shows that the highest mean positive deviation of 12.1 per cent occurred in the first quarter of 2007 and two quarters before sale prices peaked, suggesting that buyer bidding behaviour was changing prior to the market peak. The extent of market change is highlighted by the mean negative deviation of 8.6 per cent for the fourth quarter of 2008. The results demonstrate that volatility increases over the cycle and distributions of price differences are lower and flatter.Originality/valueThis paper breaks new ground through the analysis of differences between list and sale price in a period of high volatility in the housing market. The analysis shows how list price lags sale price on the up‐cycle but leads on the down‐cycle.

The Impact of Information Diffusion on Bidding Behavior in Secret Reserve Price Auctions

The interactive nature of the Internet promotes collaborative business models (e.g., auctions) and facilitates information-sharing via social networks. In Internet auctions, an important design option for sellers is the setting of a secret reserve price that has to be met by a buyer's bid for a successful purchase. Bidders have strong incentives to learn more about the secret reserve price in these auctions, thereby relying on their own network of friends or digital networks of users with similar interests and information needs. Information-sharing and flow in digital networks, both person-to-person and via communities, can change bidding behavior and thus can have important implications for buyers and sellers in secret reserve price auctions. This paper uses a multiparadigm approach to analyze the impact of information diffusion in social networks on bidding behavior in secret reserve price auctions. We first develop an analytical model for the effect of shared information on individual bidding behavior in a secret reserve price auction with a single buyer facing a single seller similar to eBay's Best Offer and some variants of NYOP. Next, we combine the implications from our analytical model with relational data that describe the individual's position in social networks. We empirically test the implications of our analytical model in a laboratory experiment, and examine the impact of information diffusion in social networks on bidding behavior in a field study with real purchases where we use a virtual world as proxy for the real world. We find that the amount and dispersion of information in the individualized context, and betweenness centrality in the social network context, have a significant impact on bidding behavior. Finally, we discuss the implications of our results for buyers and sellers.

Testing errors, supplier segregation, and food safety

AbstractDiagnostic tests provide valuable information to buyers about credence attributes such as food safety and GMO content. Errors in testing, however, can mislead buyers and lead to problems such as adverse selection. The ability to segregate suppliers who can deliver safe food from suppliers who cannot depends on the accuracy of the test procedure. In this article we examine the effect of test sensitivity, specificity, and sampling error on the ability to segregate safe and unsafe suppliers. We find that there is a maximum level of error below which unsafe suppliers are deterred from accepting a utility maximizing buyer's bid price. The maximum error depends on the probability that a supplier's production is unsafe and the cost of producing an unsafe lot, among other things. Understanding this relationship makes it possible to design contracts and government regulations that discourage unsafe suppliers from trading.

Testing Errors, Supplier Segregation, and Food Safety

Diagnostic tests provide valuable information to buyers about credence attributes like food safety and GMO content. Errors in testing, however, can mislead buyers and lead to problems like adverse selection. The ability to segregate suppliers who can deliver safe food from suppliers who cannot depends on the accuracy of the test procedure. In this article we examine the effect of test sensitivity, specificity, and sampling error on the ability to segregate safe and unsafe suppliers. We find that there is a maximum level of error below which unsafe suppliers are deterred from accepting a utility maximizing buyer's bid price. The maximum error depends on the probability that a supplier's production is unsafe and the cost of producing an unsafe lot, among other things. Understanding this relationship makes it possible to design contracts and government regulations that discourage unsafe suppliers from trading.

Human Capital, Shared Expertise, and the Likelihood of Impasse in Corporate Acquisitions

Human capital often cannot be acquired in efficient labor markets due to poor information or firm-specific skills that develop over time. Since such knowledge may be critical to firms building a strategic capability, it is not surprising that many acquisitions occur in human capital-intensive industries. Yet, the uncertainty associated with human capital increases the risk of overbidding. If the buyer bids conservatively, the target may reject the offer or rival bidders may emerge. In contrast, aggressive bidders may need to back out of the transaction if due diligence reveals unanticipated risks. Either way, impasse is more likely for targets in human capital-intensive industries. This study explores whether a shared expertise mitigates these hazards. Findings suggest that similar expertise is particularly important when acquiring human capital-intensive targets. Transactions involving unrelated buyers of such targets are less likely to close. This has implications for diversification theory and the resource-based view.

Human Capital, Shared Expertise, and the Likelihood of Impasse in Corporate Acquisitions

Human capital often cannot be acquired in efficient labor markets due to poor information or firm-specific skills that develop over time. Since such knowledge may be critical to firms building a strategic capability, it is not surprising that many acquisitions occur in human capital-intensive industries. Yet, the uncertainty associated with human capital increases the risk of overbidding. If the buyer bids conservatively, the target may reject the offer or rival bidders may emerge. In contrast, aggressive bidders may need to back out of the transaction if due diligence reveals unanticipated risks. Either way, impasse is more likely for targets in human capital-intensive industries. This study explores whether a shared expertise mitigates these hazards. Findings suggest that similar expertise is particularly important when acquiring human capital-intensive targets. Transactions involving unrelated buyers of such targets are less likely to close. This has implications for diversification theory and the resource-based view.

THE BRITISH EXPERIENCE OF PRIVATIZATION AND REGULATION OF THE ELECTRICITY INDUSTRY, AND SOME LESSONS FOR THE PHILIPPINES

The electricity industry was the UK's biggest privatization. It was complex, since part of the industry was a natural monopoly, but some of it could be broken up into competing enterprises. Service imperatives and political considerations inhibited the optimum break-up of generation. Efficiency has improved and real prices have fallen since divestiture, but it is arguable how much scope for radical competitive improvement remains. Imminent reforms under NETA are controversial and, some economists claim, misconceived, risking a new system, like the old, implemented without enough forethought. The Philippines electricity system urgently needs reform, though privatization is problematic, given existing long-term highcost supply contracts. However, reform proceeds, having absorbed some lessons from UK experience: generation has more players, and spatial pricing and separate buyer bidding should avoid some pitfalls of the UK pool. Problems of capital scarcity, imperfections in market information and ineffectual competition policy are likely to empower existing players.

The US SO 2 auction: analysis and generalization

The new Acid Rain Program allows electric utilities all over the US to trade SO 2 emission permits. An annual discriminative and revenue-neutral auction is linked to this program. This innovative SO 2 auction distributes 3% of the permits in circulation and takes place at the Chicago Board of Trade. Early results showed that the auction generated a clearing price signal lower than the market price. Now the auction price seems to be determined by the outside market price. Still, in the important starting phase, a non-discriminative auction design would probably have generated better price signals since potentially severe downward biases in both seller price offers and buyer bids are due to the discriminative feature of the EPA auction.