Bidding In Auctions Research Articles

The impact of a home purchase restrictions (HPR) policy on the distressed property market in Beijing

Distressed property sales, which are carried out under court order by online auction, have increased rapidly in China in recent years. We examine how the implementation of home purchase restrictions (HPR) by the Chinese government in 2017 has affected the market for distressed properties in Beijing. We first use a stylized second-price auction model to derive theoretical predictions for how HPR should impact the number of auction entrants, auction prices/premia, and auction bidders’ average willingness to pay. We then test those predictions using data on court sales from the Taobao online auction platform from 2015 to 2018. By comparing legal auctions of housing before and after April 2017, we obtain empirical results consistent with the model's predictions. Specifically, our analysis documents that auction entrants fell by approximately 70% and auction premia by 16%–27% after the HPR policy was implemented. Furthermore, the average bidder's willingness to pay relative to the seller's valuation fell from 80% to 40%, which suggests that bidders who were affected by HPR had higher valuations, on average, than bidders who weren't.

Earning While Learning: An Adversarial Multi-Armed Bandit Based Real-Time Bidding Scheme in Deregulated Electricity Market

As the specific incarnation of cyber-physical-social systems, in deregulated electricity market, the market gaming behaviors may have significantly affected the costs of electricity delivered to the market. Especially, from the supply side, the primary goal of power generating companies (PGCs) is to develop strategic biddings to maximize their profits in long-term trading, when facing intrinsic uncertainty. Typically, in such repeated and dynamic settings, one fundamental challenge is that, any PGC neither has prior knowledge about all unknown opponents’ incentives, nor observes their strategies and obtained profits. Especially, the common setting is that, once the bidding auction has occurred, the PGC only observes the market clearing price (MCP) at each round, and winning or losing status. While it is typical to assume some perfect or bounded rationality model of the PGCs, their real behaviors do not follow such assumptions due to lack of complete information, computational intractability, or lack of perfect execution, etc. We formulate the problem of sequentially optimizing any PGC's bids with an adversarial multi-armed bandit (MAB) model. Specifically, at each round, a PGC chooses to play against all other opponents from an infinite set of possible strategies that are split into continuous intervals by sequentially occurred MCPs. Then at the end of each round, the PGC observes the outcome of the auction and updates its estimation on the expected bid's fitness for each interval (i.e., how much the expected profit of the interval could be achieved), and selects the bid for the next round using the proposed algorithm Exp3C (i.e., exponential-weight for exploration and exploitation with continuous value). The experimental results based on real dataset demonstrate that Exp3C performs better than other heuristic schemes including pure greedy, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\varepsilon}}$</tex-math></inline-formula> -greedy and MCP predication based bidding schemes. Moreover, we theoretically prove the upper bound of average Exp3C regret per round follows <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{O}} ({2/\sqrt {\boldsymbol{T}} })$</tex-math></inline-formula> , where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> is the number of total rounds. In summary, the proposed Exp3C has two distinguished advantages. First it is distributed, since its decisions uniquely depend on its past decisions and profits. Second, it is rational, since a PGC is given guarantees on its own accumulated profit regardless of other PGCs’ behaviors.

Detecting Collusive Shill Bidding in Commercial Online Auctions

Online auctions are increasingly used as a smart and efficient way to optimise the consumers’ and sellers’ utility. A recently active field of research is the detection of fraud in online auctions. One of the most difficult types of fraud to detect is collusive shill bidding, where multiple user accounts jointly drive up the bids in an auction. This paper revises the Collusive Shill Bidding Algorithm(CSBD) proposed by Majadi et al. (2019) to develop an algorithm that is applied to a data set from an online auction platform (TBAuctions). We find that our algorithm converges, that computation time can be significantly reduced by appropriate choice of parameters, and we identify Shill Bidding for this data set, although the accuracy of the algorithm cannot be tested because of lack of ground truth values for the data. The paper further discusses steps needed for application of the algorithm to (very) large data sets, using a multiple core server, which despite substantial reduction in computation time would still require too much time to foresee a rapid implementation in real-time.

Signalling in auctions for risk-averse bidders.

We study risk-averse equilibrium bidding in first-price and second-price sealed-bid auctions where bidders have signalling concerns, i.e., they care about how the auction outcome is interpreted by an outside observer. We find that when the winner’s identity and her payment are revealed to the outside observer, risk aversion yields less aggressive bidding behaviour in the second-price sealed-bid auction than in the risk-neutral case. Our analysis explains various revenue ranking reversals relative to the risk-neutral equilibrium observed in a recent experiment by Bos (2021).

On the Role of Ad Networks: To Endogenize or Not to Endogenize the Number of Bidders in Auctions?

Abstract In the world of online advertising, demand for banner slots by advertisers is matched with the inventory available at different publishers by an intermediary (Ad Network or an Exchange). One important feature of auctions in the online advertising space is that publishers typically have multiple slots and advertisers are not necessarily interested in purchasing one unit but are rather interested in purchasing thousands of impressions. Furthermore, an ad network may have different types of publishers with varying quality of advertising space available. Consequently, bidders may value slots in one set of publishers very differently from slots in a different set. For instance, firms selling financial products and services may value slots at CNN.com’s financial section or WSJ.com very differently from slots available at people.com or even the weather section in CNN.com. So, the dilemma confronting an ad network that has inventory from different publishers, facing demand from say, advertisers selling financial products is whether to pool the inventory and conduct a single auction or conduct separate auctions so that the advertisers know that they are bidding for slots on CNN.com’s financial section or on WSJ.com and not for slots on the weather section on CNN.com or some other less preferred slots. Given the critical role that ad networks play, in serving the request of advertisers to get their advertising banners displayed in online media we examine the economic incentives of these intermediaries to derive implications for the optimal market design. More specifically, we seek answers to the following questions. Given the variation in the quality of inventory available from different publishers under what market conditions should the intermediary pool the inventory across the different publishers and conduct a single (undisclosed) auction and when would it be more profitable to conduct different (disclosed) auctions? Given a fixed number of bidders, if the intermediary chooses to conduct two auctions how many bidders should be allocated to each auction and how do market parameters such as the number of bidders or the inventory available of each type affect the allocation rule. Finally, if the intermediary chooses to conduct two auctions should they charge the same commission or different commissions in each auction? We find that when the number of advertisers is small then pooling inventory and conducting a single auction is the optimal strategy. Under these conditions when the inventory of the publishers is sufficiently differentiated it may even be optimal for the intermediary to conduct a single auction but ignore the inventory of the publisher that is valued lower. When the number of advertisers is large, we find very interestingly that conducting multiple auctions is not always optimal. Indeed, when the inventory of publishers is sufficiently differentiated conducting a single auction and ignoring the inventory of the publisher that is valued lower can still be optimal. We also identify market conditions when conducting two auctions and charging a single commission in both markets is more profitable than conducting two auctions and charging separate commissions (and vice versa).

Technical Note—Bidding in Multidimensional Auctions When the Qualities of All Bidders Matter

Measuring the “True” Quality by Rival Bidder Qualities in Score Auctions In a procurement auction, where price and quality are evaluated by a score, the assessment criteria partially account for the “true” quality offered. The joint information of all the bids can increase the reliability of the measurement process of quality, giving better accuracy of estimation compared with the reported quality. In “Technical Note―Bidding in Multidimensional Auctions When the Qualities of All Bidders Matter,” Lorentziadis examines auctions where the score is adjusted by the qualities of all bidders, who exhibit different production efficiencies and additive separable costs. At equilibrium, the total procurement cost remains the same in the first-score and the second-score auctions. For different adjustments of the score—for example, using a weighted average of all the qualities—we find that the standard unadjusted score auction brings the highest total project cost to the buyer. Procurement managers should give consideration to score rules that account for all bidder qualities.

Predictive Analytics for Real-time Auction Bidding Support: a Case on Fantasy Football

This work reports about an end-to-end business analytics experiment, applying predictive and prescriptive analytics to real-time bidding support for fantasy football draft auctions. Forecast methods are used to quantify the expected return of each investment alternative, while subgradient optimization is used to provide adaptive online recommendations on the allocation of scarce budget resources. A distributed front-end implementation of the prescriptive modules and the rankings of simulated leagues testify the viability of this architecture for actual support.

주택소비자심리지수와 매매수급동향이 아파트 경매 낙찰가율에 미치는 영향- 강남구, 서초구, 송파구를 중심으로

The housing market consumer index, the trends of sales price index and the auction bid price ratio are used as indicators to judge and the real estate market. Whereas there have been several studies that analyzed the relationship between the auction bid price ratio and the housing market consumer index, studies on the trends of sales price index are insufficient. This study examined the determinants of the auction bid price ratio in apartment auctions, and through empirical analysis with the housing market consumer index and the trends of sales price index, it tried to find out how the two indices affect the real estate auction market. The study set the time scope to 6 years between January, 2015 and December, 2020 and the space scope to Gangnam-gu, Seocho-gu, and Songpa-gu of Seoul. Independent variables were set as housing market, auction, local, location, article, and time of a successful bid. The analysis method used multiple linear regression analysis using a hedonic price model. As a result of analysis, it was found that the housing market consumer index and the trends of sales price index have a positive effect on the auction bid price ratio. Moreover, the failure in bidding, the land registration, the tenants with opposing power, and the lien have a negative effect on the auction bid price ratio, and the number of bidders has a positive effect on the auction bid price ratio. In addition, more than 30 years of elapsed years, small and medium sized space, and medium-large sized space have a positive effect on the auction bid price ratio, and the timepoint of 2018, 2019, and 2020 were found to have a positive effect on the auction bid price ratio. It is expected that auction bidders will be able to make rational decisions by judging and predicting the situation of the real estate market and auction market.

Shill bidding and information in eBay auctions: A Laboratory study

In online auction platforms, like eBay, sellers have frequently been observed to bid on their item to artificially increase its price, and this is known as shill bidding. We represent the eBay auction in a sequential auctions environment using lab experiments and study the behavioral consequences of sellers being able to participate as shill bidders and of being informed about buyers’ past bidding histories. We find that the possibility of shill bidding in ongoing and future auctions benefits sellers and mostly affects high private-value buyers. At the same time, buyers seem to overreact to the threat of shill bidding in the future auctions by biding too high in the current auction. However, providing sellers with buyers’ bidding histories between auctions has little impact on auction outcomes and players’ bidding behavior. Moreover, there are significant differences between buyers’ and sellers’ dynamic bidding behavior during auctions, which can be used to identify shill bidding sellers from buyers.

Revisiting the convergence theorem for competitive bidding in common value auctions

Revisiting the convergence theorem for competitive bidding in common value auctions

Do institutional investors’ corporate site visits impact seasoned equity offering discounts? Evidence from detailed investor bids in SEO auctions

We examine the impact of institutional investor corporate site visits on seasoned equity offering (SEO) discounts. Using unique databases that give the identities of institutional investors making corporate site visits and the same investors making bids on SEOs, we document that when institutional investors make more corporate site visits before SEOs, they bid higher for the SEOs, leading to lower SEO discounts and this effect is more pronounced when institutional investors face higher information uncertainty. Additional analysis suggests that institutional investors have the ability to select high future performance SEO firms after their corporate site visits. The findings provide direct investor-level evidence consistent with that equity issuers can alleviate the adverse selection problem in the SEO process through hosting more site visits of potential investors.

A Description of Game Theory

Game theory implies the logic of problematic situations with solutions through mathematical calculation. Since 1944, the game theory has been applied to military decisions, educational materials, and to find methods for complicated circumstances. The equilibria, different strategies to maximize surplus, and analysis of games could be inferred to assist players. The dominant strategy is the esse.ntial portion of the research to the game where the choice results in the best payoffs not affected by the other players' decisions. Closely connected to the dominant strategy, the Nash equilibrium represents the idea of the stable point where there is no profitable deviation for any player. Moreover, through calculation, the mixed strategy could be acquired, in situations when the probability of choices of all players is random, in the probability distribution. Finally, studies of shading the bid in auctions could maximize the surplus of bidders in partial auctions. The research could accommodate deliberate determinations in various games.

Paradox of Concessions in Developing Countries

This paper presents a game-theoretic analysis of concessions that explicitly considers both the concession auction and the subsequent operation game. The concession contract requires investment to be made, but the concessionaire may benefit from underinvesting and ex-post renegotiating with the regulator. The paper highlights the “Paradox of Concession”: the more successful the auction is, the higher is the probability of underinvestment. We propose a new mechanism, based on benefits for investment rather than punishment for underinvestment. The new mechanism: (i) is efficient; (ii) increases auction bids; (iii) eliminates the “paradox of concessions”; (iv) and can be fine-tuned to reduce the likelihood of underinvestment.

Robust inference in first-price auctions: Overbidding as an identifying restriction

Laboratory experiments find consistently that bidding in first-price auctions tends to be more aggressive than predicted by the risk-neutral Bayesian Nash Equilibrium (RNBNE) — a finding known as the overbidding puzzle. Several alternative models can explain the overbidding puzzle, but no canonical alternative to RNBNE has emerged. Instead of estimating a particular model of overbidding, we use the overbidding restriction itself for identification, which allows us to bound the valuation distribution and the seller’s revenue under counterfactual reserve prices in the spirit of Haile and Tamer (2003). These bounds are consistent with RNBNE and all models of overbidding, and the bounds remain valid even if there is unobserved heterogeneity in bidding strategies. We evaluate the validity of the bounds numerically and in experimental data.

Modeling bidding decisions and bid markup size for construction projects: A fuzzy approach

Previous studies of construction contract auction bidding have mainly focused on finding factors affecting the markup and decision to bid (d2b), without considering expert weight and critical factors as input variables for estimating the size of markup needed. This study develops a 3-step Mamdani-type of Fuzzy Inference System (FIS) identifying critical factors and predicting markups for construction projects. The first step models the wights of 31 construction experts based on their characteristics (i.e., experience and academic qualifications). The second step models frequency, severity, and importance weights for each factor to find the rank and priority of the factors, revealing the critical factors to be current workload, project (contract) size, need for work, availability of labor and staff required, project owner, and duration. The third step takes the importance weight of each factor from the previous step and the contractor’s evaluation of the frequency of the factors as input variables to predict the bid markup. The model is demonstrated and tested with two actual construction projects having actual markups of 20% and 45%, and the predicted markups are found to be 26.3% and 42% respectively, which ensures reliable outcomes in assessing contractors’ bidding decisions. It is a novel model that can simultaneously identify critical factors and predict an optimal markup in assisting contractors’ d2b for the construction auctions and developing risk management plans. Future research can optimize this model by incorporating competitors’ bids and enhancing prediction accuracy to guarantee the lowest price with a reasonable profit margin.

Anchoring effects in repeated auctions of homogeneous objects: Evidence from Macao

We provide evidence of anchoring effects from the sealed-bid auctions of vehicle plates in Macao. Vehicle plates are homogeneous in use value and differ only in character and numerical sequences. Nevertheless, vehicle plates with particular numerical sequences are consistently and repeatedly auctioned off in Macao, thus providing unique panel data that facilitate the identification of an anchoring effect. We adopt a hedonic pricing model to estimate the rational price of vehicle plates. We find that when bidding on a numerical sequence, bidders anchor on the winning bid in the previous auction, which result in continuation of the winning bid price.

부동산경매에서 외지인의 농지낙찰 특성 및 결정요인에 관한 연구

The speculation of agricultural land by non-farmers in the recent LH Corporation scandal has raised a huge social issue and caused quite a stir. As the price of agricultural land is rising, it is used as a means of increasing property for a capital gain instead of farming, which is clearly confirmed in the percentage of outsiders purchasing agricultural land. Specifying these issues, this study investigates the characteristics of outsiders making a successful bid in agricultural land auctions and checks factors influencing their successful bids in the procedure of a real estate auction as one of the important ways to acquire land. As for methodology, the investigator examined 1,606 items of agricultural land (fields and paddies) that were auctioned off in court in five major areas around the nation between 2013~2017, dividing them according to outsiders and local people based on the addresses of successful bidders and conducting binary logistic regression analysis. The analysis results demonstrate that the percentage of outsiders in the successful bids of agricultural land was very high in real estate auctions and identified characteristic factors (waterfront areas, stake items, and so on) that had impacts on the successful bids of agricultural land by outsiders.

Nonparametric estimation of first price auctions via density–quantile function

We propose a nonparametric estimator of bidders’ value function based on a kernel estimator of the density–quantile function of bids in first price auctions. This estimator provides certain advantage over the conventional approach that relies on the distribution/density ratio of the bids. We use a boundary-adaptive kernel for boundary bias correction and propose a practical method of bandwidth selection. Our numerical experiments demonstrate good performance of the proposed method in the estimation of first price auctions with risk neutral and risk averse bidders.

Strong substitutes: structural properties, and a new algorithm for competitive equilibrium prices

We show the strong substitutes product-mix auction bidding language provides an intuitive and geometric interpretation of strong substitutes as Minkowski differences between sets that are easy to identify. We prove that competitive equilibrium prices for agents with strong substitutes preferences can be computed by minimizing the difference between two linear programs for the positive and the negative bids with suitably relaxed resource constraints. This also leads to a new algorithm for computing competitive equilibrium prices which is competitive with standard steepest descent algorithms in extensive experiments.

Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs

This paper addresses the question of how much to bid to maximize the profit when trading in two electricity markets: the hourly Day-Ahead Auction and the quarter-hourly Intraday Auction. For optimal coordinated bidding many price scenarios are examined, the own non-linear market impact is estimated by considering empirical supply and demand curves, and a number of trading strategies is used. Additionally, we provide theoretical results for risk neutral agents. The application study is conducted using the German market data, but the presented methods can be easily utilized with other two consecutive auctions. This paper contributes to the existing literature by evaluating the costs of electricity trading, i.e. the price impact and the transaction costs. The empirical results for the German EPEX market show that it is far more profitable to minimize the price impact rather than maximize the arbitrage.