Topic
Bidding
About: Bidding is a research topic. Over the lifetime, 15371 publications have been published within this topic receiving 294233 citations. The topic is also known as: competitive bidding.
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TL;DR: In this paper, the authors model the maintenance of management quality through the simultaneous functioning of internal and external corporate control mechnism, and examine how the information sets of the board and the acquiror are noisily aggregated.
167 citations
12 Aug 2012
TL;DR: This paper presents a bid-optimization approach that is implemented in production at Media6Degrees for bidding on these advertising opportunities at an appropriate price and combines several supervised learning algorithms, as well as second price auction theory, to determine the correct price.
Abstract: Billions of online display advertising spots are purchased on a daily basis through real time bidding exchanges (RTBs). Advertising companies bid for these spots on behalf of a company or brand in order to purchase these spots to display banner advertisements. These bidding decisions must be made in fractions of a second after the potential purchaser is informed of what location (Internet site) has a spot available and who would see the advertisement. The entire transaction must be completed in near real-time to avoid delays loading the page and maintain a good users experience. This paper presents a bid-optimization approach that is implemented in production at Media6Degrees for bidding on these advertising opportunities at an appropriate price. The approach combines several supervised learning algorithms, as well as second price auction theory, to determine the correct price to ensure that the right message is delivered to the right person, at the right time.
167 citations
TL;DR: In this article, a nonzero sum stochastic game theoretic model and a reinforcement learning (RL)-based solution framework are presented to assess market power in day-ahead (DA) energy markets.
Abstract: Auctions serve as a primary pricing mechanism in various market segments of a deregulated power industry. In day-ahead (DA) energy markets, strategies such as uniform price, discriminatory, and second-price uniform auctions result in different price settlements and thus offer different levels of market power. In this paper, we present a nonzero sum stochastic game theoretic model and a reinforcement learning (RL)-based solution framework that allow assessment of market power in DA markets. Since there are no available methods to obtain exact analytical solutions of stochastic games, an RL-based approach is utilized, which offers a computationally viable tool to obtain approximate solutions. These solutions provide effective bidding strategies for the DA market participants. The market powers associated with the bidding strategies are calculated using well-known indexes like Herfindahl-Hirschmann index and Lerner index and two new indices, quantity modulated price index (QMPI) and revenue-based market power index (RMPI), which are developed in this paper. The proposed RL-based methodology is tested on a sample network
167 citations
Posted Content•
TL;DR: In this article, the authors examine how experience affects the decisions of individual investors and institutions in IPO auctions to bid in subsequent auctions, and their bidding returns, and find that high returns in previous IPO auctions increase the likelihood of participating in future auctions.
Abstract: We examine how experience affects the decisions of individual investors and institutions in IPO auctions to bid in subsequent auctions, and their bidding returns. We track bidding histories for all 31,476 individual investors and 1,232 institutional investors across all 84 IPO auctions during 1995-2000 in Taiwan. For individual bidders: (1) high returns in previous IPO auctions increase the likelihood of participating in future auctions; (2) bidders’ returns decrease as they participate in more auctions; (3) auction selection ability deteriorates with experience; and (4) bidders with greater experience bid more aggressively. These findings are consistent with naive reinforcement learning wherein individuals become unduly optimistic after receiving good returns. In sharp contrast, there is little sign that institutional investors exhibit such behavior.
167 citations
TL;DR: In this paper, the authors present a dynamic model of takeovers based on the stock market valuations of merging firms, which incorporates competition and imperfect information and determines the terms and timing of takingovers by solving option exercise games between bidding and target shareholders.
Abstract: This paper presents a dynamic model of takeovers based on the stock market valuations of merging firms. The model incorporates competition and imperfect information and determines the terms and timing of takeovers by solving option exercise games between bidding and target shareholders. The implications of the model for returns to stockholders are consistent with the available evidence. Notably, the model predicts that (1) returns to target shareholders should be larger than returns to bidding shareholders, and (2) returns to bidding shareholders can be negative if there is competition for the acquisition of the target. In addition, the model generates new predictions relating these returns to the drift, volatility and correlation coefficient of the bidder and the target stock returns and to the dispersion of beliefs regarding the benefits of the takeover.
167 citations