Optimal Auction Duration: A Price Formation Viewpoint

Optimal Auction Duration in Financial Markets In the considered auction market, market makers fill the order book during a given time period while some other investors send market orders. The clearing price is set to maximize the exchanged volume at the clearing time according to the supply and demand of each market participant. The error made between this clearing price and the efficient price is derived as a function of the auction duration. We study the impact of the behavior of market takers on this error to minimize their transaction costs. We compute the optimal duration of the auctions for 77 stocks traded on Euronext and compare the quality of the price formation process under this optimal value to the case of a continuous limit order book. Continuous limit order books are usually found to be suboptimal. Order of magnitude of optimal auction durations is from 2–10 minutes.

Optimal auctions through deep learning

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30--40 years of intense research, the problem remains unsolved for settings with two or more items. We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions auctions. In this approach, an auction is modeled as a multilayer neural network, with optimal auction design framed as a constrained learning problem that can be addressed with standard machine learning pipelines. Through this approach, it is possible to recover to a high degree of accuracy essentially all known analytically derived solutions for multi-item settings and obtain novel mechanisms for settings in which the optimal mechanism is unknown.

Optimal Auction Design With Common Values: An Informationally Robust Approach

A profit‐maximizing seller has a single unit of a good to sell. The bidders have a pure common value that is drawn from a distribution that is commonly known. The seller does not know the bidders' beliefs about the value and thinks that beliefs are designed adversarially by Nature to minimize profit. We construct a strong maxmin solution to this joint mechanism design and information design problem, consisting of a mechanism, an information structure, and an equilibrium, such that neither the seller nor Nature can move profit in their respective preferred directions, even if the deviator can select the new equilibrium. The mechanism and information structure solve a family of maxmin mechanism design and minmax information design problems, regardless of how an equilibrium is selected. The maxmin mechanism takes the form of a proportional auction: each bidder submits a one‐dimensional bid, the aggregate allocation and aggregate payment depend on the aggregate bid, and individual allocations and payments are proportional to bids. We report a number of additional properties of the maxmin mechanisms, including what happens as the number of bidders grows large and robustness with respect to the prior over the value.

A Multi-Attribute Reverse Auction Model on Margin Bidding

Multi-attribute reverse auction has many advantages for the buyer with the multi-dimensional attribute requirements. However, it is hard to design an optimal auction mechanism for the scenario in practice. Therefore, this paper presents a method transforming multi-attribute auction into single-attribute auction by bidding in deposit. The analysis indicates that our method can reduce not only the transaction risk caused by the supplier’s bid abandonment but also the operating cost and complexity of the multi-attribute auction. Besides, our method meets the incentive compatibility and participation constraint conditions by promising that the highest bidding supplier is the winner in the Auction and can obtain higher expected profits than traditional auctions for the buyer.

Robust Auctions for Revenue via Enhanced Competition

In “Robust Auctions for Revenue via Enhanced Competition,” T. Roughgarden, I. Talgam-Cohen, and Q. Yan revisit the classic Bulow–Klemperer result. This result compares the revenues of two well-known auction formats: the welfare-maximizing Vickrey auction and the revenue-maximizing Myerson auction. It shows that, with an extra bidder competing for the item, the Vickrey auction becomes as good as the Myerson auction in terms of revenue while maintaining independence from prior distributional information about bidders’ valuations. Unfortunately, Myerson’s toolbox for revenue-optimal auction design does not extend to combinatorial auctions with multiple heterogenous items, for which optimizing revenue remains a challenge—especially if we want auction designs that are simple and robust enough to use in practice. This paper extends the Bulow–Klemperer result to multiple heterogenous items by showing that a prior-independent, simple, welfare-maximizing auction with additional competing bidders achieves as much revenue as the ill-understood optimal auction.

On Optimal Auctions for Mixing Exclusive and Shared Matching in Platforms

Platforms create value by matching participants on alternate sides of the marketplace. Although many platforms practice one-to-one matching (e.g., Uber), others can conduct and monetize one-to-many simultaneous matches (e.g., lead-marketing platforms). Both formats involve one dimension of private information, a participant’s valuation for exclusive or shared allocation, respectively. This paper studies the problem of designing an auction format for platforms that mix the modes rather than limit to one and, therefore, involve both dimensions of information. We focus on incentive-compatible auctions (i.e., where truthful bidding is optimal) because of ease of participation and implementation. We formulate the problem to find the revenue-maximizing incentive-compatible auction as a mathematical program. Although hard to solve, the mathematical program leads to heuristic auction designs that are simple to implement, provide good revenue, and have speedy performance, all critical in practice. It also enables creation of upper bounds on the (unknown) optimal auction revenue, which are useful benchmarks for our proposed auction designs. By demonstrating a tight gap for our proposed two-dimensional reserve-price-based mechanism, we prove that it has excellent revenue performance and places low information and computational burden on the platform and participants. This paper was accepted by Chris Forman, information systems.