In the construction industry, the winner’s curse occurs when the winning contractor has underestimated the project’s true cost. Using a game and auction theory approach, this study aims to analyze - and potentially reduce - industry exposure to the effects of the winner's curse in construction bidding. A simulation model for single and multi-stage bidding processes was developed and analyzed an actual dataset of California Department of Transportation projects. The majority of general contractors and sub-contractors suffer from the winner's curse in both single and multi-stage bidding environments. The multi-stage bidding environment incurs more losses than the single-stage bidding environment. Through learning from past experiences though, the multi-stage bidding environment provides contractors with better opportunity to avoid the winner's curse. Finally, it was shown that the symmetric risk neutral Nash equilibrium optimal bid function provides the contractors with a tool to avoid the winner's curse and gain strategic positive profits.

Railways have played an important role for the Swedish economy throughout the 20th into the 21st century since it provides good connections between urban and rural regions and also within them. However, it has been very costly for the state to run this activity and a significant effort to reduce costs and to increase the efficiency for this sector was to introduce procurement auctions where also private firms are invited to lay bids. Four auctions have taken place in Jönköpings Län since its introduction in 1990. It has been a turbulent time economically for the winning bidders and the phenomena winner’s curse is evident where the bidder with highest over-estimate wins and therefore faces high costs or low returns. This study points at flaws in the auction design as the reason for the economical difficulties for the winning firms. If second-price sealed bid auctions or first-price sealed bid auctions were used instead of a combination of first-price sealed auction and English auction, the winner’s curse phenomena could be reduced or even eliminated.

<p>Railways have played an important role for the Swedish economy throughout the 20th into the 21st century since it provides good connections between urban and rural regions and also within them. However, it has been very costly for the state to run this activity and a significant effort to reduce costs and to increase the efficiency for this sector was to introduce procurement auctions where also private firms are invited to lay bids.</p><p>Four auctions have taken place in Jönköpings Län since its introduction in 1990. It has been a turbulent time economically for the winning bidders and the phenomena winner’s curse is evident where the bidder with highest over-estimate wins and therefore faces high costs or low returns.</p><p>This study points at flaws in the auction design as the reason for the economical difficulties for the winning firms. If second-price sealed bid auctions or first-price sealed bid auctions were used instead of a combination of first-price sealed auction and English auction, the winner’s curse phenomena could be reduced or even eliminated.</p>

This dissertation consists of two essays on the topic of bidding in multi-unit common value auction. Essay one examines the role of capacity constraint on the auction results and bidding behavior. We consider a general case where bidders are unconstrained, and a second setting where bidders are capacity constrained. We document downward sloping demand curves for individual bidders. Bidders shade their bids by submitting quantity-price pairs and spreading their bids. The winner's curse is strong in the unconstrained treatment, but we find no evidence of the winner's curse when bidding constraints are imposed. Unconstrained bidders shade bids significantly more and their quantity-weighted prices are much lower than those in the constrained treatment. Interacting with the information structure, the capacity constraint has a significant impact on the auction results including the market clearing price, market efficiency, and the degree of market concentration. We provide evidence that efficient price discovery in multi-unit auctions with diverse information is possible, but careful attention to auction design will make this outcome more likely. Essay two examines how the introduction of a noncompetitive bidding option affects outcomes in a multi-unit uniform-price auction. The experimental design incorporates many of the characteristics of the markets that pertain to the issuance of new equity securities. Important features of the bidding environment include endogenous bidder entry, costly information acquisition, bidders that differ by capacity constraint, and substantial uncertainty with respect to the intrinsic value. We use a standard uniform-price auction as our baseline setting where only competitive bids are accepted. Our results show that introducing the noncompetitive bidding option improves auction performance by increasing revenue and reducing price error. Underpricing is found in both treatments, but is less severe in the presence of the noncompetitive bidding option. The incorporation of this option significantly increases both the small bidder participation rate and allocation, and reduces the incentive for small bidders to free ride by submitting extremely high bids. Under both treatments, information acquisition increases large bidders' profits but proves unprofitable for small bidders, and pricing accuracy is increasing in the rate of information acquisition.

Problem: A widely accepted perception within the research community is that initial public offerings tend to be underpriced on a global level. Numerous studies have been conducted across global markets to identify the underlying factors. However, the findings have been heterogeneous and contradictory. Within the context of underpricing, the Nordic market, including Sweden, is relatively underexplored compared to the extensively researched American market. This research study aims to investigate several crucial variables, based on previous international research, and their relationship to this phenomenon within an area that remains insufficiently explored. Purpose: The goal of this study is to contribute to the existing IPO literature, by examining the factors that have statistically significant associations with underpricing in IPOs on the Swedish market. Method: The study has utilized empirical quantitative methodology with a deductive approach, and has relied on secondary data. It has examined short-term returns, specifically the returns generated on the first day of trading. Hypotheses regarding the study's five independent variables have been deduced from existing international empirical evidence. These variables are (1) industry affiliation, (2) company size (measured by revenue), (3) company age, (4) debt ratio, and (5) stock exchange regulations (measured through markets). By using multiple regression analysis, it has been possible to determine whether any of these variables have a statistically significant association with underpricing in IPOs. Results/Conclusion: The study reported non-statistically significant findings, which contradict the majority of previous scientific research. However, without regard to the regression analysis, weak support remained for certain independent variables showing vague associations with the studied phenomenon. / Problem: En vedertagen uppfattning inom forskarvärlden är att börsintroduktioner har en generell tendens att vara underprissatta på en global nivå. Många undersökningar har genomförts på världens marknader i syfte att identifiera de underliggande faktorerna. Resultaten har emellertid varit heterogena och motsägelsefulla. Inom ramen för underprissättning är den nordiska marknaden, med Sverige som en del av den, jämförelsevis outforskad i förhållande till den amerikanska som varit föremål för betydligt mer omfattande forskning. Forskningsstudien ämnar undersöka ett antal avgörande variabler, baserade på tidigare internationell forskning, och dess koppling till fenomenet inom ett område som ännu inte är tillräckligt utforskat. Syfte: Föreliggande studie har som mål att bidra till den befintliga börsintroduktionslitteraturen, genom att utreda vilka faktorer som har statistiskt signifikanta samband med underprissättning vid börsintroduktioner på den svenska marknaden. Metod: Studien har använt empirisk kvantitativ metodik med deduktiv ansats, och har utgått från sekundärdata. Studien har undersökt den kortsiktiga avkastningen, mer specifikt; den avkastning som skapats under första handelsdagen. Hypoteser om studiens fem oberoende variabler har deducerats från befintliga internationella empiriska underlag. Dessa är; (1) branschtillhörighet, (2) företagsstorlek (mätt som omsättning), (3) företagsålder, (4) skuldsättningsgrad och (5) börshandelsregler (mätt genom marknader). Med hjälp av en multipel regressionsanalys, har det varit möjligt att utreda om någon av dessa variabler uppvisat ett statistiskt signifikant samband med underprissättningen. Resultat/Slutsats: Studien rapporterade icke-statistiskt signifikanta fynd vilket motsäger merparten av tidigare vetenskaplig forskning. Emellertid, utan hänsyn till regressionsanalysen, återstod svaga stöd för att vissa oberoende variabler kunnat visa vaga samband med det studerade fenomenet.

Previous studies have underscored the economic importance of the role of the entrepreneur, and empirical studies testing the nature of the entrepreneur are notably lacking. This study directly addresses this issue by examining newly gathered field data which captures the decision making and risk behaviors for a group of high-technology entrepreneurs. Two decision making tasks were used to elicit risk aversion measures and to test for any 'joy of winning' or judgmental errors, possibly in the form of over optimistic behavior. These elicitations were made with the use of multiple price formats and winner's curse experiments. 62 responses were collected from subjects at the 2004 national Small Business Innovation Research (SBIR) conference in Atlanta, March 2004. From these 62 responses a subject pool of 33 entrepreneurs and 29 non-entrepreneurs were identified. Statistical methods were employed to assign risk aversion measures and identify any 'joy of winning' or judgmental errors for the entrepreneur (treatment) group compared with the non-entrepreneur (control) group. Findings show that entrepreneurs exhibit less risk aversion, but show no statistically meaningful difference in judgmental errors compared to their non-entrepreneur counterparts. However, there is evidence to support the claim that both entrepreneurs and non-entrepreneurs exhibit a 'joy of winning', and that the size of the effect is larger for entrepreneurs.

One of the puzzles regarding IPO’s is that the issuers rarely get upset about leaving substantial amounts of money on the table due to underpricing. The cost of underpricing is the number of shares sold multiplied by the difference between the first-day closing price and the offer price. The research sample of IPOs and JSE databases comprised, respectively, 160 and 321 new applicants for the years 1995-1999. New applicants comprising the research sample raised R12.55 billion with an underpricing cost exceeding R2.85 billion i.e., 22.71 percent of the IPO capital raised. This cost was found to be nearly 10 times greater than the R295 million paid in fees to the corporate advisors by the issuing companies. The prime beneficiaries of this discount were a select grouping of private placement investors at the discretion of the corporate advisors and directors. Mean unadjusted initial first day returns amounted to 55.04 percent. Public Offer IPO’s (solely or as a component of a Hybrid Offer) follow UK influenced corporate legal systems– both in legislative norm and empirical results. First day initial returns were presented per issuer List Board, Method and Type of Listing, IPO capital raised and disclosed use of proceeds. Internationally many theories have been raised as to what has become to be known as the short-run underpricing puzzle. The winner’s curse hypothesis is directly tested flowing from and the unique data availability. It was shown that South African Public and Hybrid IPO Offer methods bear an exceptionally close correlation to UK influenced corporate legal systems and as such proved a reliable empirical testing ground for the winners curse phenomena using the same methodology and equations as their international counterparts hereunder. The UK based corporate law and institutional arrangements in South Africa allow a direct test of the empirical implications of the winner’s curse hypothesis in pricing unseasoned new issues.

We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.

We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.

In this study, we explore models for optimal bidding in auctions on the bond market using data gathered from the Bloomberg Fixed Income Trading platform and MIFID II reporting. We define models that aim to fulfill two purposes. The first is to hit the best competitor price, such that a dealer can win the trade with the lowest possible margin. This model should also take into account the phenomenon of the Winner's Curse, which states that the winner of a common value auction tends to be the bidder who overestimated the value. We want to avoid this since setting a too aggressive bid could be unprofitable even when the dealer wins. The second aim is to define a model that estimates a quote that allows the dealer to win a certain target ratio of trades. We define three novel models for these purposes that are based on the best competitor prices for each trade, modeled by a Skew Exponential Power distribution. Further, we define a proxy for the Winner's Curse, represented by the distance of the estimated price from a reference price for the trade calculated by Bloomberg which is available when the request for quote (RFQ) arrives. Relevant covariates for the trades are also included in the models to increase the specificity for each trade. The novel models are compared to a linear regression and a random forest regression method using the same covariates. When trying to hit the best competitor price, the regression models have approximately equal performance to the expected price method defined in the study. However, when incorporating the Winner's Curse proxy, our Winner's Curse adjusted models are able to reduce the effect of the Winner's Curse as we define it, which the regression methods cannot. The results of the models for hitting a target ratio show that the actual hit ratio falls within an interval of 5% of the desired target ratio when running the model on the test data. The inclusion of covariates in the models does not impact the results as much as expected, but still provide improvements with respect to some measures. In summary, the novel methods show promise as a first step towards building algorithmic trading for bonds, but more research is needed and should incorporate more of the growing data set of RFQs and MIFID II recorded transaction prices. / I denna studie utforskar vi modeller för optimal budgivning för auktioner på obligationsmarknaden med hjälp av data som samlats in från plattformen Bloomberg Fixed Income Trading och MIFID II-rapportering. Vi definierar modeller som ämnar att uppfylla två syften. Det första är att träffa det bästa konkurrentpriset så att en handlare kan vinna auktionen med minsta möjliga marginal. Denna modell bör också ta hänsyn till fenomenet Winner's Curse, som innebär att vinnaren av en så kallad common value auction tenderar att vara den budgivare som överskattat värdet. Vi vill undvika detta eftersom det kan vara olönsamt att skicka ett alltför aggressivt bud även om handlaren vinner. Det andra syftet är att definiera en modell som uppskattar ett pris som gör det möjligt för handlaren att vinna en viss andel av sina obligationsaffärer. Vi definierar tre nya modeller för dessa ändamål som bygger på de bästa konkurrentpriserna för varje transaktion vi har data på. Dessa modelleras av en Skew Exponential Power-fördelning. Vidare definierar vi en variabel som indirekt mäter fenomenet Winner's Curse, representerad av budprisets avstånd från ett referenspris för transaktionen beräknad av Bloomberg som är tillgänglig när en request for quote (RFQ) anländer. Relevanta kovariat för transaktionen implementeras också i modellerna för att öka specificiteten för varje transaktion. De nya modellerna jämförs med en linjärregression och en random forest-regression som använder samma kovariat. När målet är att träffa det bästa konkurrentpriset ger regressionsmodellerna ungefär samma resultat som expected price-modellen som definieras i denna studie. När man däremot integrerar effekten av Winner's Curse med den definierade indirekta variablen kan vår Winner's Curse-justerade modell minska effekten av Winner's Curse, vilket regressionsmetoderna inte kan. Resultaten av modellerna som ämnar vinna en förbestämd andel av transaktionerna visar att den faktiska andelen transaktioner som man vinner faller inom ett intervall på 5% kring den önskade andelen när modellen körs på testdata. Att inkludera kovariat i modellerna påverkar inte resultaten till den grad som uppskattades, men ger mindre förbättringar med avseende på vissa mättal. Sammanfattningsvis visar de nya metoderna potential som ett första steg mot att bygga algoritmisk handel för obligationer, men mer forskning behövs och bör utnyttja mer av den växande datamängden av RFQs och MIFID II-rapporterade transaktionspriser.