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.

Dynamics of bid optimization in online advertisement auctions

Abstract: We consider the problem of online keyword advertising auctions among multiple bidders with limited budgets, and study a natural bidding heuristic in which advertisers attempt to optimize their utility by equalizing their return-on-investment across all keywords. We show that existing auction mechanisms combined with this heuristic can experience cycling (as has been observed in many current systems), and therefore propose a modified class of mechanisms with small random perturbations. This perturbation is reminiscent of the small time-dependent perturbations employed in the dynamical systems literature to convert many types of chaos into attracting motions. We show that the perturbed mechanism provably converges in the case of first-price auctions and experimentally converges in the case of second-price auctions. Moreover, the point of convergence has a natural economic interpretation as the unique market equilibrium in the case of first-price mechanisms. In the case of second-price auctions, we conjecture that it converges to the "supply-aware" market equilibrium. Thus, our results can be alternatively described as a tâtonnement process for convergence to market equilibriumin which prices are adjusted on the side of the buyers rather than the sellers. We also observe that perturbation in mechanism design is useful in a broader context: In general, it can allow bidders to "share" a particular item, leading to stable allocations and pricing for the bidders, and improved revenue for the auctioneer.

Repurchase Tender Offers, Signaling, and Managerial Incentives

Abstract: One of the main principles of corporate finance is that managers should maximize the market value of the outstanding securities. While it is realized that managers and security holders may have divergent objectives, it is generally assumed that various market forces keep managerial and shareholders' goals in line. Out of the extensive literature on this issue, three such market forces emerge. First, non-value-maximizing firms are prime targets for take-over bids (e.g., see [14]): bidding firms could acquire control over the shares of the target firm, replace the management, follow a value-maximizing strategy, and realize a profit from the resulting appeciation of the target shares. Second, outside shareholders may charge managers-owners ex ante by discounting stock prices for expected managerial expropriation, which may induce managers to accept various restrictions on their behavior (e.g., see [11]). Third, shareholders may charge managers ex post, indirectly via the discipline imposed by a competitive managerial labor market (e.g., [8]). Note the difference from the previous mechanism: Jensen and Meckling [11] assume that managerial wages are fixed so that all the adjustment for expected expropriation is reflected in stock prices (and, ultimately, in costly monitoring and bonding devices). In Fama's [8] model, all the adjustment occurs in the managerial labor market, so that the value of the firm remains unaffected by expected managerial expropriation.

Optimal real-time bidding for display advertising

Abstract: In this paper we study bid optimisation for real-time bidding (RTB) based display advertising. RTB allows advertisers to bid on a display ad impression in real time when it is being generated. It goes beyond contextual advertising by motivating the bidding focused on user data and it is different from the sponsored search auction where the bid price is associated with keywords. For the demand side, a fundamental technical challenge is to automate the bidding process based on the budget, the campaign objective and various information gathered in runtime and in history. In this paper, the programmatic bidding is cast as a functional optimisation problem. Under certain dependency assumptions, we derive simple bidding functions that can be calculated in real time; our finding shows that the optimal bid has a non-linear relationship with the impression level evaluation such as the click-through rate and the conversion rate, which are estimated in real time from the impression level features. This is different from previous work that is mainly focused on a linear bidding function. Our mathematical derivation suggests that optimal bidding strategies should try to bid more impressions rather than focus on a small set of high valued impressions because according to the current RTB market data, compared to the higher evaluated impressions, the lower evaluated ones are more cost effective and the chances of winning them are relatively higher. Aside from the theoretical insights, offline experiments on a real dataset and online experiments on a production RTB system verify the effectiveness of our proposed optimal bidding strategies and the functional optimisation framework.

Towards a Competitive Market for Reactive Power

Abstract: This paper presents the design of a competitive market for reactive power ancillary services. Generator reactive power capability characteristics are used to analyze the reactive power costs and subsequently to construct a bidding framework. The reactive power market is settled on uniform price auction, using a compromise programming approach based on a modified optimal power flow model. The paper examines market power issues in these markets and identifies locations where strategic market power advantages are present that need to be removed through investments in reactive power devices.