Number Of Bidders Research Articles

Reserve Price (In)equivalence: Shill Bidding to Lower the Reserve Price

ABSTRACTThe optimal reserve price of the third‐price auction with two bidders differs from that with more than two bidders, whereas the optimal reserve price of the second‐price auction is the same regardless of the number of bidders. We generalize this result to the kth‐price multiunit auctions with single‐unit demand, and introduce a new kind of shill‐bidding incentive of bidders in order to lower the reserve price. Alongside budget constraints caused by overbidding, this may explain why third‐price auctions are rare in practice.

Using machine learning to investigate the determinants of loan default in P2P lending: Are there differences between before and during COVID-19?

The coronavirus disease (COVID-19) has led to a persistent increase in the volatility of the credit market and triggered a series of financial distress and bankruptcy. To investigate whether there are differences in loan default determinants before and during COVID-19 and to identify the most effective predictors of loan default during COVID-19, this study employs machine learning methods to establish a comprehensive loan default prediction model for Peer-to-peer (P2P) lending based on four perspectives: loan characteristics, credit transaction history, personal information, and macroeconomic environment. The results show that the EXtreme Gradient Boosting (XGBoost) outperforms the other models and that credit transaction history plays a vital role in forecasting loan default over the two periods. We also find discrepancies between the effects of consumer price index, purchasing manager’ index, and the number of bidders on loan default before and during the pandemic. Our study contributes to related research fields on loan default prediction by identifying loan default determinants that are more applicable to unstable periods and investigating the impact of COVID-19 on default predictions. Meanwhile, our findings can provide P2P lending investors, platforms, and policymakers with practical implications to reduce uncertainty and losses that result from similar black swan events.

Sequential elimination in multi-stage all-pay auctions

I study a multi-stage all-pay auction in which the lowest bidder in each stage is eliminated. Elimination continues until only two bidders remain, one of whom wins the auction. I analyse optimal bidding behaviour and the seller’s expected revenue when bidders have independent and private values. In contrast to typical bidding strategies, the optimal bid in each stage is strictly decreasing in the number of bidders. For a fixed number of bidders, however, bids increase as bidders progress through the stages of the auction. Despite independent values, this multi-stage auction yields less expected revenue to the seller than its single-stage counterpart when there are more than three bidders.

Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

PurposeFactors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.Design/methodology/approachIn this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.FindingsThe findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.Practical implicationsThe model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.Social implicationsAlthough the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.Originality/valuePrevious research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

지역여건을 고려한 아파트 경매의 매각가격 결정요인

This study empirically analyzes the determinants of apartment sale prices in the auction market, focusing on apartments auctioned in Daegu as of 2021. Specifically, it aims to examine whether various factors based on regional characteristics influence sale prices and to explore the detailed impact of residential density. The main empirical findings are as follows. Auction characteristics were all significant. Forced auctions and the number of bidders increased sale prices, while the number of unsuccessful bidders and unpaid management fees decreased sale prices. In terms of Characteristics of goods, the construction period, parking spaces per household, and small and medium-sized area showed significant results. The elapsed years reduced sale prices, while parking spaces per household and small and medium-sized area increased sale prices. In terms of location characteristics, subway distance and school distance were significant. However, it was analyzed that the influence on sales prices was very low. Finally, the study looked at the impact of residential amenities, which hadn't been considered before. Over half of the sub-variables showed significance. Dense residential areas were found to increase sale prices, indicating a preference for housing with various amenities. The results of this study confirmed the determinants of apartment auction sale prices. Specifically, it was established that dense residential areas yield high sale prices in the auction market, attributed to their relatively superior amenities. This study suggests that equal amenities are needed to stabilize the prices of local housing markets.

Predicting Real-World Penny Auction Durations by Integrating Game Theory and Machine Learning

Game theory and machine learning are two widely used techniques for predicting the outcomes of strategic interactions among humans. However, the game theory-based approach often relies on strong rationality and informational assumptions, while the machine learning-based approach typically requires the testing data to come from the same distribution as the training data. Our work studies how to integrate the two techniques to address these weaknesses. We focus on the interactions among real bidders in penny auctions, and develop a three-stage framework to predict the distributions of auction durations, which indicate the numbers of bids and auctioneer revenues. Specifically, we first leverage a pre-trained neural network to encode the descriptions of products in auctions into embeddings. Second, we apply game theory models to make preliminary predictions of auction durations. In particular, we tackle the challenge of accurately inferring parameters in game theory models. Third, we develop a Multi-Branch Mixture Density Network to learn the mapping from product embeddings and game-theoretic predictions to the distributions of actual auction durations. Experiments on real-world penny auction data demonstrate that our framework outperforms both game theory-based and machine learning-based prediction approaches.

Feasibility Study of Fixed Olympic Cities Based on TOPSIS Evaluation Model

Olympic Games have been facing a steady decline in the number of bids, with countries experiencing varying degrees of negative impact, making the cost of hosting the Games unaffordble for most countries. Therefore, the paper tends to find a strategy for hosting the Olympic Games that suits the current state of the world and address the issue of choosing a host country for the Games. This paper developed an Olympic Games host city assessment model to identify two permanent hosts for the Games and make recommendations to the International Olympic Committee. Then the paper analyzed the feasibility of the two countries chosen as hosts for the Olympic Games and carried out a predictive analysis of Beijing, China and Salt Lake City, USA for putting each sub-indicator in the predictive analysis. Sensitivity analysis is used to verify the impact of potential strategies on our metrics. The paper discusses the implications of the policy and the pros and cons of it.

Critical Factors Influencing Bid Mark-up Decision in Public Street Project – A Review

Deciding a bid price with an appropriate mark-up added to the estimated project cost is essential for the contractors' business survival. However, the competitive environment had influenced contractors to offer the lowest bid price, leading to the submission of unrealistically low prices to win bids. This practice can impact disputes and adversarial relationships between parties during construction and provide an additional risk to the project delay, cost overrun, quality compromise and failure to complete the project. Therefore, this paper aims to provide an overview of the literature on critical factors influencing bid mark-up decisions in public street projects. The objectives are to identify methods and tools, including the critical factors in determining the contractors' bid mark-up decision. The approach is based on analysing pertinent publications on the theme. The top five (5)ranked factors influencing contractors' decision on bid mark-up size can be found in the literature: the number of bidders, the competitiveness of other bidders, relationship and experience with clients, experience on similar projects and project size. Besides that, the determination of mark-up size is based on experience, intuition, past bidding results, past bidding patterns ofcompetitors, the client's tender estimate, the client's record, and financial ability.

Firm Competitive Structure and Consumer Reaction in Search Advertisings

Sponsored search advertising has become an important venue for firms competing for consumers. As a result, many keywords attract a large number of bidders, and the competing advertisers may be quite heterogeneous. We examine whether this heterogeneity impacts how consumers perceive and react to such competitions. To this end, we draw on the theory of strategic groups to prescribe the structure of the competitive environment and investigate how strategic groups impact consumers’ clicking and website-visit behavior when viewing sponsored search results. Our unique datasets that combine search results from Google and consumers’ clickstream data enable us to disentangle such an impact. We find strong positive externality for within-group competitors relative to across-group competitors: (1) consumers are more likely to co-visit two firms that belong to the same strategic group, as opposed to two firms from different groups when both firms appear in the search results; (2) the presence of a firm in the search results primes consumers to visit other firms from the same strategic group even when the other firms do not appear in the search results. Our findings contribute to the sponsored search and strategic group literature by theorizing and empirically verifying consumers’ website-visit behaviors from the strategic group perspective.

OPTIMIZATION OF THE AUCTION DURATION IN THE PRESENSE OF TIME-DEPENDANT COSTS

This paper examines the influence of the duration of auctions or tenders on the expected gain of their organizer. Extending the duration of bidding affects auction results in two ways. On the one hand, it allows attracting a larger number of participants to the auction, and the competition between them increases the chances of the auctioneer to get a better price. On the other hand, delaying bids delays the receipt of money (for auctions) or required goods or services (for tenders), and time has value in itself. The influence of these two factors, which act in opposite directions, suggests the existence of an optimal duration of the bidding process. The paper develops a mathematical model of bidding, which formalizes these considerations and provides an algorithm to determine their optimal duration. The arrival of bidders willing to participate in the auction is modeled as a Poisson process. Each participant is characterized by his own assessment of the value of the object put up for auction. These estimates are assumed to be independent identically distributed random variables drawn from some parametric distribution. Under these assumptions, Myerson's revenue equivalence theorem makes it possible to predict the expected results of the auction as a function of the number of bidders, regardless of the auction rules. On this basis, it is possible to compare the benefits and costs associated with changing the duration of time for accepting applications for participation in bidding, which makes it possible to determine its optimal value. The obtained optimality conditions have a meaningful and intuitive economic interpretation. For practical applications, the use of Monte Carlo methods based on the empirical distribution of bid and ask prices is proposed. The practical implementation of the proposed algorithm can improve the economic performance of the auctioneer, which is especially relevant for the public sector of the economy.

Auctions Versus Fixed Prices: Lessons from a Procurement Pilot

Abstract Objectives Private health care providers are often acquired through procurement auctions to produce publicly financed services. In this study, we examine the costs and benefits of auctions compared to fixed price procurements. We analyze a procurement pilot that was conducted by Kela, the largest rehabilitation organizer in Finland. Kela piloted fixed prices instead of an auction in two different services in 2020. Data and methods Our data includes all procurements both for inpatient and outpatient rehabilitation courses by Kela in years 2012-22. The data includes 455 procurement auctions, which received 2584 bids, and 4 pilot procurements of fixed prices, which received 166 bids. Altogether 10 providers were contracted in the pilot procurements of the first service whereas the last two pilot procurements attracted 153 providers. Our preliminary results are based on simple statistical and graphical analysis regarding bid prices and their dispersion. Results Our preliminary main finding is that competition, calculated as number of bids per auction, is correlated with prices in the procurements. The main challenge of fixed prices is to determine the appropriate price-level. We find that the prices were set to be attractive in the pilots, as all providers who previously participated in auctions accepted the price, and several new providers entered the market in the pilot procurements of the second service. Conclusions Efficiently organized public procurements can yield large fiscal saving for the public economy. The procurements give information about the number of providers offering the service and their price. Fixed pricing eliminates the competitive setting, which means that the price is often set too high to ensure the availability of services. Key messages • Efficiently organized public procurements can yield large fiscal saving for the public economy. • Fixed pricing eliminates the competitive setting, which means that the price is often set too high to ensure the availability of services.

Hedonic analysis of the price of mangalarga marchador horses sold at auctions

The objective was to identify sets of relevant atributes for pricing Mangalarga Marchador horses at auctions and to estimate a hedonic pricing function. We analyzed 452 horses at 20 auctions. The studied variables were characteristics intrinsic to the animals and attributes that influence sale price, which were identified through generalized linear and logistic model analysis. For each additional prize and additional bid, the prices decreased by R$ 2110.00 and R$ 549.00 respectively. The individual’s maternal grandsire positively or negatively affected prices, from R$ - 86.70 to R$ + 35.50. Non-pregnant females were penalized in price by R$ 110.00 compared to non-castrated males. A bay coat added R$ 215.00 to prices when compared to chestnut animals. Online auctions added R$ 20.10 to prices and virtual auctions penalized prices by R$ 617.00. The gray coat devalues a sale by 48% and Seller 3 decreasing by 18%. We conclude that auction type, number of prizes, share sold, animal category, coat, seller, number of bids, and maternal grandsire influenced the prices of Mangalarga Marchador horses, and that coat and seller that influenced the probability of the animal being sold.

Improving Sales in Online Auctions: The Impact of Emphasizing Corporate Social Responsibility in Item Descriptions

ABSTRACT This study explores how emphasizing corporate social responsibility (CSR) in item descriptions relates to purchasing decisions for consumers buying goods through online auctions. We leverage herd mentality theory, the heuristic–systematic model (HSM), and signaling theory to develop a framework of how online auctions may be impacted by item descriptions touting CSR. We then utilize text analysis to examine 23,932 auctions and analyze how emphasizing CSR in item descriptions influenced actual purchase opportunities. In this study, we find that including more CSR terminology in an item description increases sales both directly and indirectly, via an increase in the number of bids. These findings can be leveraged by sellers to better promote their products, by using terminology that targets the desire of bidders to experience positive psychosocial and values-affirming sentiments when making purchases.

Estimation of electronic procedures effects in public procurement under favoritism

This paper investigates the effects of electronic procedures for supplier selection in public procurement multi criteria auctions (with a selection of a winner based not only on the price criterion) for the results of research and development (R&D) work in Russia. The electronic procedures in public procurement can increase the number of bidders and lead to lower final contract prices. However, in the presence of favoritism, the effect of electronic procedures may be limited. To identify and evaluate the effects of introduction of electronic procedures, we collected microdata on 4517 composite auctions for R&D results for the period from 12/16/2016 to 12/20/2021. Until 2019, there were practically no electronic multi criteria auctions, and starting from 2019, all multi criteria auctions were in electronic form, which creates quasi-experimental conditions. Favoritism is evaluated based on the frequency of interaction between the customer and the supplier. The results of econometric modeling suggest that the introduction of the electronic procedures do increase competition in multi criteria auctions and lead to lower final contract prices, but the effect is weaker in auctions with potentially affiliated customer and supplier. Moreover, one of the main channels of the negative effect of favoritism is inflated quality criterion scores of affiliated suppliers.

Intensifying Competition in Public Procurement

The intensity of competition is typically measured according to the number of bidders on any given public procurement market. By analysing the Tenders Electronic Daily database 2017–2020, we examine the conditions in the contract notices which have an impact on increasing competition in launching public procurement procedures. The research findings show that the longer duration of the contract, the lowest price awarding criteria, the division of the subject matter into lots and the possibility of negotiations are essential elements which can activate bidders. These are unequivocal prerequisites for creating effective and fair competition in public procurements.

Entrepreneur weirdness as a double‐edged sword: Effects on product creativity and investor attraction

AbstractMany iconic entrepreneurs have been celebrated for being unapologetically weird. Using pitches collected from the TV show Shark Tank, we seek to unpack the link between entrepreneur weirdness and investor interest (i.e., number of bidders) in the context of securing investor funding. Integrating Wood and colleagues’ (2007) theory of non‐normativity with Amabile's (1983, 1996) componential theory of creativity, we propose that weirdness, as a form of non‐normativity, yields both positive and negative outcomes for entrepreneurs through two distinct pathways. Specifically, the weirdness advantage operates through entrepreneur creativity, whereas the weirdness liability operates through lower entrepreneur competence. Our empirical analyses of non‐normativity suggest that entrepreneur weirdness indeed is a double‐edged sword. Further, we propose that entrepreneur warmth (being friendly and good natured) moderates both weirdness effects, by strengthening the positive effect on entrepreneur creativity and dampening the negative effect on entrepreneur competence. Implications of the advantages and disadvantages of entrepreneur weirdness are discussed.

농지 경매 매각가율 결정요인 연구

In this study, an empirical analysis was conducted using a hedonic pricing model to determine the factors affecting the farmland auction sales price ratio in Hwaseong-City. The results show that the variables that have a positive(+) effect are road interface(others), successful bid price, number of bidders, and the variables that have a negative(-) effect are number of failed bids, planned management area(etc.), farm area were found to have an effect. The size of the influence on the farmland sales price ratio is in the order of the number of failed bids, road interface(others), planning management(etc.), farmland area, successful bid price, and number of bidders. The contributions of this study are that the farmland variables that form the value of farmland have been used to identify the factors that affect the farmland auction sales price ratio by utilizing the cases of farmland auctions that have won farmland auctions in the court auction, centering on Hwaseong-City. By dividing and analyzing the data, it provided reliable data for decision-making to the participants in the farmland auction.

사과 실험경매에서 값싼 대화는 공돈 효과를 제거할 수 있는가?

In the experimental auction, participation fees can cause a house money effect that makes the value of the product higher than the actual value. The purpose of this study is to analyze whether the house money effect can be eliminated in experimental auctions by applying cheap talk used in contingent valuation and choice experiment to reduce hypothetical bias. For the analysis, an experimental auction is conducted by organizing five different auction products depending on whether or not to provide information on the taste of apples, method, color, and sugar content. The number of experimental groups is 8, and four groups provide a cheap talk, and four groups do not. The number of bidders per group is about 15, and the number of rounds is 9. The results show that it has been empirically verified that cheap talk can eliminate the house money effect. The results also indicate that as the auction round progresses, the younger the age or the lower the education level, the greater the effect of removing the house money effect of the cheap talk. Since cheap talk is easily applicable and effective, the use of cheap talk is recommended in studies using experimental auction.

Round offer prices in M&A transactions: costly negotiation and psychological preference

PurposeThe authors of this study aim to investigate possible explanations of the prevalence of price clustering in the final offer prices of mergers and acquisitions (M&A).Design/methodology/approachThe authors use final offer price in M&A deals to investigate the price clustering phenomena. The authors used regressions and logistic regressions to examine potential factors that might affect pricing strategy by looking into one-time acquirers and experienced serial acquirers.FindingsPrice clustering increases with negotiation uncertainties characterized as competitive bidding, number of bidders, challenged deals and duration. Moreover, the authors find persistent price clustering in experienced serial acquirers that are more experienced and better equipped with handling uncertainties, suggesting a preference of using round numbers regardless of levels of uncertainties. The authors' evidence shows that price clustering results from a combination of Harris' (1991) costly negotiation hypothesis where round prices may be used to lower search costs and psychological bias and preference.Originality/valueThe authors appear to be the first to investigate alternative theories that support M&A offer price clustering behavior, finding that both the costly negotiation and psychological bias and preference theories apply to M&A final price formation. Thus, the authors' major contribution, specific to the M&A process, is a clarification of physical and psychological factors associated with bidding and negotiation behavior. The authors are confident that the authors' study impacts conventional knowledge regarding M&A deal negotiation strategies, including bidding behavior, contract negotiation, financial analysis, management practices and risk management.

Maximize Liquid Welfare in Combinatorial Auctions with Monotone Valuations

In this paper, we consider how to maximize the liquid welfare in a combinatorial auction where bidders have monotone valuations and are budget constrained. We study the setting that budgets are public information and present a universally truthful, budget feasible and computationally-efficient randomized [Formula: see text]-approximate mechanism, where [Formula: see text] is the number of items and [Formula: see text] is the number of bidders, respectively.