Bidder Profits Research Articles

To Thine Own Self Be True: Asymmetric Information in Procurement Auctions

As procurement auctions increasingly move to digital platforms, more data and information is available (or can be made available) to bidders. Despite this trend, relatively little is known about the impact of information asymmetries in these settings. We investigate two such differences in first‐price sealed‐bid reverse auctions with a common value. In a design that mirrors real construction procurement auctions, our laboratory experiment tests the impact of the precision of a bidder's cost estimate and the degree to which bidders know the inherent cost estimate precisions in the auction. We find that more understanding of estimate precision decreases bidder profit, counter to our expectation; however, we also find evidence of strategic behavior from those bidders that ratchets up pressure on competitors and pushes competitors toward bankruptcy. Most notably, understanding just one's own precision can help avoid the winner's curse in some settings. The same result does not apply if bidders also know their competitor's precision; more information does not help. The implication from our realistic setting—that reduced uncertainty may not help the bidder—raises important questions about the degree of transparency that is optimal in procurement auctions.

To Thine Own Self Be True: Asymmetric Information in Procurement Auctions

As procurement auctions increasingly move to digital platforms, more data and information is available (or can be made available) to bidders. Despite this trend, relatively little is known about the impact of information asymmetries in these settings. We investigate two such differences in first-price sealed-bid reverse auctions with a common value. In a design that mirrors real construction procurement auctions, our laboratory experiment tests the impact of the precision of a bidder’s cost estimate and the degree to which bidders know the inherent cost estimate precisions in the auction. We find that more understanding of estimate precision decreases bidder profit, counter to our expectation; however, we also find evidence of strategic behavior from those bidders that ratchets up pressure on competitors and pushes competitors toward bankruptcy. Most notably, understanding just one’s own precision can help avoid the winner’s curse in some settings. The same result does not apply if bidders also know their competitor’s precision; more information does not help. The implication from our realistic setting – that reduced uncertainty may not help – raises important questions about the degree of transparency that is optimal in procurement auctions.

Real-Time Bid Optimization for Group-Buying Ads

Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by daily-deal providers. Unlike traditional web ads, the advertiser’s profits for group-buying ads depend on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts and dynamically vary the bids to maximize profits. Furthermore, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real-time ad selection, admissibility, and real-time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection, and admission strategies on a multimillion click stream of 935 ads. The proposed real-time bidding, selection, and admissibility show significant profit increases over the existing strategies. Further experiments illustrate the robustness of the bidding and acceptable computation timings.

Should Sellers Prefer Auctions? A Laboratory Comparison of Auctions and Sequential Mechanisms

When bidders incur a cost to learn their valuations, bidder entry can impact auction performance. Two common selling mechanisms in this environment are an English auction and a sequential bidding process. Theoretically, sellers should prefer the auction, because it generates higher expected revenues, whereas bidders should prefer the sequential mechanism, because it generates higher expected bidder profits. We compare the two mechanisms in a controlled laboratory environment, varying the entry cost, and find that, contrary to the theoretical predictions, average seller revenues tend to be higher under the sequential mechanism, whereas average bidder profits are approximately the same. We identify three systematic behavioral deviations from the theoretical model: (1) in the auction, bidders do not enter 100% of the time; (2) in the sequential mechanism, bidders do not set preemptive bids according to the predicted threshold strategy; and (3) subsequent bidders tend to overenter in response to preemptive bids by first bidders. We develop a model of noisy bidder-entry costs that is consistent with these behaviors, and we show that our model organizes the experimental data well. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1800. This paper was accepted by Teck Ho, behavioral economics.

Bounds in auctions with unobserved heterogeneity

Many empirical studies of auctions rely on the assumption that the researcher observes all variables that make auctions differ ex ante. When there is unobserved heterogeneity, the direction of the bias this causes is known only in a few restric- tive examples. In this paper, I show that ignoring unobserved heterogeneity in a first price sealed bid auction with symmetric independent private values gives bounds on several quantities of economic interest under surprisingly general con- ditions. The results apply to certain quantities related to expectations of valua- tions, including bidder profits (which can be used to recover bid preparation costs in entry models) and the efficiency loss of assigning the object randomly. I then turn to estimation of these bounds, and show that, when only the winning bid is available, the rate of convergence can be slower than the square root of the num- ber of auctions observed and depends on the number of bidders. These results ap- ply more generally to estimation of functionals of a distribution from repeated ob- servations of an order statistic and may be of independent interest. I apply these methods to bound the efficiency loss from replacing a set of procurement auc- tions for highway construction in Michigan with random assignment. Keywords. Auctions, unobserved heterogeneity, L-statistics, nonstandard asymp- totics. JEL classification. C14, C44.

On being balanced in an unbalanced world

This paper examines the case of a procurement auction for a single project, in which the breakdown of the winning bid into its component items determines the value of payments subsequently made to bidder as the work progresses. Unbalanced bidding, or bid skewing, involves the uneven distribution of mark-up among the component items in such a way as to attempt to derive increased benefit to the unbalancer but without involving any change in the total bid. One form of unbalanced bidding for example, termed Front Loading (FL), is thought to be widespread in practice. This involves overpricing the work items that occur early in the project and underpricing the work items that occur later in the project in order to enhance the bidder's cash flow. Naturally, auctioners attempt to protect themselves from the effects of unbalancing—typically reserving the right to reject a bid that has been detected as unbalanced. As a result, models have been developed to both unbalance bids and detect unbalanced bids but virtually nothing is known of their use, success or otherwise. This is of particular concern for the detection methods as, without testing, there is no way of knowing the extent to which unbalanced bids are remaining undetected or balanced bids are being falsely detected as unbalanced. This paper reports on a simulation study aimed at demonstrating the likely effects of unbalanced bid detection models in a deterministic environment involving FL unbalancing in a Texas DOT detection setting, in which bids are deemed to be unbalanced if an item exceeds a maximum (or fails to reach a minimum) ‘cut-off’ value determined by the Texas method. A proportion of bids are automatically and maximally unbalanced over a long series of simulated contract projects and the profits and detection rates of both the balancers and unbalancers are compared. The results show that, as expected, the balanced bids are often incorrectly detected as unbalanced, with the rate of (mis)detection increasing with the proportion of FL bidders in the auction. It is also shown that, while the profit for balanced bidders remains the same irrespective of the number of FL bidders involved, the FL bidder's profit increases with the greater proportion of FL bidders present in the auction. Sensitivity tests show the results to be generally robust, with (mis)detection rates increasing further when there are fewer bidders in the auction and when more data are averaged to determine the baseline value, but being smaller or larger with increased cut-off values and increased cost and estimate variability depending on the number of FL bidders involved. The FL bidder's expected benefit from unbalancing, on the other hand, increases, when there are fewer bidders in the auction. It also increases when the cut-off rate and discount rate is increased, when there is less variability in the costs and their estimates, and when less data are used in setting the baseline values.

Maximizing bidder's profit in online auctions using grey system theory's predictor agent

PurposeAs the demand for online auctions increases, the process of monitoring multiple auction houses, deciding which auction to participate in and making the right bids, become challenging tasks for consumers. Hence, knowing the closing price of a given auction would be an advantage, since this information will ensure a win in a given auction. However, predicting a closing price for an auction is not easy, since it is dependent on many factors. The purpose of this paper is to report on a predictor agent that utilises grey system theory to predict the closing price for a given auction.Design/methodology/approachThe focus of the research is on grey system agent. This paper reports on the development of a predictor agent that attempts to predict the online auction closing price in order to maximise the bidder's profit. The performance of this predictor agent is compared with two well‐known techniques, the Simple Exponential Function and the Time Series, in a simulated auction environment and in the eBay auction.FindingsThe grey theory agent gives a better result when less input data are made, while the Time Series Agent can be used with the availability of a lot of information. Although the Simple Exponential Function Agent is able to predict well with less input data, it is not an appropriate method to be applied in the prediction model since its formula is not realistic and applicable in predicting the online auction closing price. The experimental results also showed that using moving historical data produces a higher accuracy rate than using fixed historical data for all three agents.Originality/valueGrey system theory prediction model, GM(1, 1) has not been applied in online auction prediction. In this paper the authors have applied grey theory into an agent to predict the closing price of an online auction, in order to increase the profit of bidders in the bidding stage. The experimental results show that the accuracy of the grey prediction model is more then 90 per cent, with less then eight historical data inputs.

A Laboratory Comparison of Auctions and Sequential Mechanisms

When bidders incur a cost to learn their valuations, bidder entry can impact auction performance. Two common selling mechanisms in this environment are an English auction, and a sequential bidding process. Bulow and Klemperer (2009) show, theoretically, that sellers should prefer the auction, because it generates higher expected revenues, while bidders should prefer the sequential mechanism, because it generates higher expected bidder profits. We compare the two mechanisms in a controlled laboratory environment, varying the entry cost, and find that, contrary to the theoretical predictions, average seller revenues tend to be higher under the sequential mechanism, while average bidder profits are approximately the same. We identify three systematic behavioral deviations from the theoretical model: (1) in the auction, bidders do not enter 100% of the time, (2) in the sequential mechanism, bidders do not set pre-emptive bids according to the predicted threshold strategy, and (3) subsequent bidders tend to over-enter in response to pre-emptive bids by first bidders. We develop a model of noisy bidder entry costs that is consistent with these behaviors, and show that our model organizes the experimental data well.

AUCTIONS, REAL OPTIONS VALUATION, AND PRIVATIZATION

This article studies competitive bidding by strategic investors to buy enterprises that are being privatized through sealed-bid auctions. The bidders use a real options approach to enterprise valuation that accounts for asset quality given their private information. It is shown that the optimal bidding strategy under first-price sealed-bid auctions defines a Bayesian Nash equilibrium in which each strategic investor bids only a fraction of its prior valuation and competition decreases the bidder's profit margin. The greater the transparency and information disclosure on asset quality, the higher the seller's expected receipts will be.

Set-Asides and Subsidies in Auctions

Set-asides and subsidies are used extensively in government procurement and resource sales. We analyze these policies in an empirical model of US Forest Service timber auctions. The model fits the data well both within the sample of unrestricted sales used for estimation, and when we predict (out-of-sample) outcomes for small business set-asides. Our estimates suggest that restricting entry substantially reduces efficiency and revenue, although it increases small business participation. An alternative policy of subsidizing small bidders would increase revenue and small bidder profit, with little efficiency cost. We explain these findings by connecting to the theory of optimal auction design. (JEL D44, H57, L73, Q23)

Designing bidding strategies in sequential auctions for risk averse agents

Designing efficient bidding strategies for sequential auctions represents an important, open problem area in agent-mediated electronic markets. In existing literature, a variety of bidding strategies have been proposed and have been shown to perform with varying degrees of efficiency. However, most of strategies proposed so far do not explicitly model bidders' attitudes towards risk which, in mainstream economic literature, is considered an essential attribute in modeling agent preferences and decision making under uncertainty. This paper studies the effect that risk profiles (modeled through the standard Arrow-Pratt risk aversion measure), have on the bidders' strategies in sequential auctions. First, the sequential decision process involved in bidding is modeled as a Markov Decision Process. Then, the effect that a bidder's risk aversion has on her decision theoretic optimal bidding policy is analyzed, for a category of expectations of future price distributions. This analysis is performed separately for the case of first price and second-price sequential auctions. Next, the bidding strategies developed above are simulated, in order to study the effect that an agent's risk aversion has on the chances of winning a set of complementary-valued items. The paper concludes with an experimental study of how the presence of risk-averse bidders affects both bidder profits and auctioneer revenue, for different market scenarios of increasing complexity.

Multi-object auctions with package bidding: An experimental comparison of Vickrey and iBEA

The use of package auctions for complex resource allocation has been rapidly increasing in recent years. In this paper, we study two package auction mechanisms in a laboratory setting, a sealed bid Vickrey auction and an ascending version of Vickrey, the iBEA auction. Unlike the single-unit Vickrey auction, where bidders tend to overbid in the laboratory, most of our bidders either underbid or bid their true values. Furthermore, at the aggregate level, while the Vickrey auction generates significantly higher revenue than does iBEA, the iBEA auction generates significantly higher bidder profit and efficiency. Additionally, a significantly larger proportion of iBEA auctions achieves 100% efficiency than does the Vickrey auction. We also find that human bidders learn from their robot opponents when the robot strategies are (myopic) best responses.

Endogenous Asymmetry in a Dynamic Procurement Auction

This paper analyzes a typical procurement market where a few firms compete repeatedly over a sequence of first price auctions. In practice, such firms have been found to experience capacity constraints whereby their costs increase with their capacity utilization. As a result, since a firm’s capacity utilization can change over time, the firms can endogenously become asymmetric in terms of their costs. We model this setting as an infinite-horizon dynamic procurement auction and numerically solve for its Markov Perfect Bayesian Equilibrium. We find that the procurer is best off scheduling frequent auctions for small project sizes. The alternative, having fewer auctions but for larger contracts, tends to make the firms more asymmetric and more capacityconstrained; this softens competition, and increases bidder profits and procurement costs. We also find that forward-looking bidding dampens the competition-softening effects of asymmetry; farsighted firms compete more intensely than myopic ones. We show that this undermines the intended goal of a bid preference style affi rmative action program: the more farsighted the firms are, the greater the rate of bid preference needs to be. JEL CLASSIFICATION: C73, D44, H57, L20

Better, Faster, Cheaper: An Experimental Analysis of a Multiattribute Reverse Auction Mechanism with Restricted Information Feedback

The majority of reverse auctions for procurement use a single-attribute (price) format while providing constraints on nonprice attributes such as quality and lead time. Alternatively, a buyer could choose to conduct a multiattribute auction where bidders can specify both a price and levels of nonprice attributes. While such an auction may provide higher theoretical utility to the buyer, it is not clear that this theoretical improvement will be realized given the increased complexity of the auction. In this research, we present an ascending auction mechanism for a buyer whose utility function is known and dependent on three attributes. Motivated by a supply chain procurement problem setting, we consider quality and lead time for the two attributes in addition to price. The auction mechanism provides the bidders with restricted feedback regarding the buyer’s utility function. We explore, experimentally, the performance of this multiattribute auction mechanism as compared to a price-only auction mechanism. Compared with the price-only auction, we find that our mechanism design is effective in increasing both buyer utility and bidder (supplier) profits.

Price discovery in combinatorial auctions using Gibbs Sampling

Considerable research discusses the advantages and disadvantages of combinatorial auctions. This study addresses a disadvantage, the loss of price discovery for the individual items sold as bundles. Prior studies confirm that there may not be a unique unit-level equilibrium price. We claim a distribution of prices satisfy a given allocation and describe a technique to determine these distributions. Gibbs Sampling allows us to discover characteristics of combinatorial auctions based on the allocated bids. We extract the market-influenced unit-level price, bidder profit, reservation discount distributions and are able to find patterns that depict synergies between products. The posterior distribution provides insights useful to managerial decision making.

Price discovery in combinatorial auctions using Gibbs Sampling

Considerable research discusses the advantages and disadvantages of combinatorial auctions. This study addresses a disadvantage, the loss of price discovery for the individual items sold as bundles. Prior studies confirm that there may not be a unique unit-level equilibrium price. We claim a distribution of prices satisfy a given allocation and describe a technique to determine these distributions. Gibbs Sampling allows us to discover characteristics of combinatorial auctions based on the allocated bids. We extract the market-influenced unit-level price, bidder profit, reservation discount distributions and are able to find patterns that depict synergies between products. The posterior distribution provides insights useful to managerial decision making.

Toeholds, takeover premium, and the probability of being acquired

Most of the theoretical literature on tender offers has been devoted to illustrating the positive effects of the toehold on the bidder's profits. Empirical research, however, shows that a high proportion of bidders do not trade on the target's shares prior to the tender offer announcement. This paper presents a model in which the bidder trades in the open market before announcing a tender offer and the incumbent shareholders form beliefs about the rival's quality given the order size. Market liquidity allows the potential bidder to partially hide her trade, and thus insiders are not able to ascertain whether an increase in volume indicates toehold acquisition. Stock price prior to the announcement date and market perception about the probability of a takeover are therefore contingent on players actions. We show that in some situations no trade will be optimal, and a negative relationship between takeover premium and toehold size arises. Interestingly, stock liquidity and initial stake are positively related. Our results also provide a theoretical basis for the observed pre-bid stock price dynamics. In particular, we show that the ratio between price runup and bid premium is increasing in the toehold size.

When Do Bidders Purchase a Toehold? Theory and Tests

Most of the theoretical literature on tender offers has been devoted to illustrating the positive effects of the toehold on the bidder's profits. Empirical research, however, shows that a high proportion of bidders do not trade on the target's shares prior to the tender offer announcement. This paper presents a model in which the bidder trades in the open market before announcing a tender offer and the incumbent shareholders form beliefs about the rival's quality given the order size. Market liquidity allows the potential bidder to partially hide her trade, and thus insiders are not able to ascertain whether an increase in volume indicates toehold acquisition. Stock price prior to the announcement date and market perception about the probability of a takeover are therefore contingent on players actions. We show that in some situations no trade will be optimal, and a negative relationship between takeover premium and toehold size arises. Interestingly, stock liquidity and initial stake are positively related. Our results also provide a theoretical basis for the observed pre-bid stock price dynamics. In particular, we show that the ratio between price runup and bid premium is increasing in the toehold size. The model's implications are then tested with a sample including tender offers in the US and the UK, estimating a bivariate generalization of the tobit model. We find a broad support for the model and significant differences across countries. We show that toeholds and probability of an acquisition are negatively related, and that companies in which the appropriation of private benefits of control is more likely have a higher probability of being taken over.

Separating controllable performance from non-controllable performance: the case of optimal procurement contracting

Recent research has refined the notion that a manager's evaluation should be based only on controllable measures of performance. Introducing an explicit measure of non-controllable performance into the evaluation has been shown to reduce agency problems by allowing compensation to have a lesser dependence on non-controllable performance. While the agency literature on controllability has suggested the benefit of including non-controllable performance measures for evaluation, the literature has not focused on how the form of the actual contracts should be adjusted when more precise measures of non-controllable performance become available. In this paper, issues concerning contract form and controllability are addressed in the context of a procurement model based on that of McAfee and McMillan ( Rand Journal of Economics , 17; 1986). A linear incentive contract that depends on: (1) an imperfect signal of non-controllable costs; and (2) total costs, is shown to Pareto dominate a contract based on total costs alone. Thus, some theoretical justification for the use of cost escalation clauses is demonstrated. It is shown that as observability of the non-controllable costs increases, the form of the optimal contract moves closer to a fixed-price contract, and the expected utility of both the purchaser and potential suppliers increases. Hence, a complete ranking for a class of costless post-decision information systems is provided. Separating out some of the non-controllable costs allows better risk-sharing and lets the purchaser sharpen the trade-off between the incentives to deal with the hidden action problem versus the hidden information problem. In the limiting case, when the signal of non-controllable costs is perfectly informative, the optimal contract is shown to be a ‘fixed profit’ contract in the sense that the winning bidder's profits (not payment) are independent of the realization of non-controllable costs.

The value of information in a sealed-bid auction

In this paper, we explore bidders' incentives to gather information in auctions, when there is one bidder with only public information and another with some private information. We find that the bidder with only public information makes no profit at equilibrium, while the bidder with private information generally makes positive profits. Moreover, the informed bidder's profits rise when he gathers extra information, and the increase in greater when the information is collected overtly than when it is collected covertly. When the uniformed bidder can observe some of the better-informed bidder's information, he prefers to make his observations covertly. If the seller has access to some of the better-informed bidder's information, or if he has affiliated information of his own, he can raise the expected price by adopting a policy of making that information public. However, there are cases where a policy of publicizing his information would lower the expected price. The distinguishing feature of these latter cases seems to be that the seller's information is complementary to the information of the better-informed bidder.