arXiv research

Study proposes a machine learning method for bid shading in first-price auctions.

Algorithm learns to bid optimally in repeated first-price auctions with censored feedback.

Deep network optimizes ad bidding for first-price auctions.

Optimizes reserve prices for first-price auctions to maximize revenue.

This paper tackles optimal bidding strategies in adversarial first-price auctions.

Optimistic Mirror Descent framework improves bidding strategies in non-stationary first-price auctions.

This paper studies an environment of simultaneous, separate, first-price auctions for complementary goods. Agents observe private values of each good before making bids, and the complementarity between goods is explicitly incorporated in their utility. For simplicity, a model is presented with two first-price auctions …

Improved learning algorithm for first-price auctions reduces regret significantly.

New algorithms minimize regret in repeated auctions by estimating values and optimizing bids.

Study optimal auction formats for maximizing MEV on Ethereum.

Algorithm learns to bid in auctions with shilling, masking real bids.

This work optimizes bid strategies for online auctions using measure-valued optimization.

The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.

Optimal algorithm for contextual bandits with unknown context distributions.

In the classical contextual bandits problem, in each round $t$, a learner observes some context $c$, chooses some action $i$ to perform, and receives some reward $r_{i,t}(c)$. We consider the variant of this problem where in addition to receiving the reward $r_{i,t}(c)$, the learner also learns the values of $r_{i,t}(c…

Market makers minimize regret by strategically setting bid and ask prices.

Over the last decade, digital media (web or app publishers) generalized the use of real time ad auctions to sell their ad spaces. Multiple auction platforms, also called Supply-Side Platforms (SSP), were created. Because of this multiplicity, publishers started to create competition between SSPs. In this setting, there…

We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution $D$ in every round). Prior work assumes that the buyer is fully rational and will perfectly reason about how their bids today affect the seller's decisio…

Paper tackles binary feedbacks in contextual search learning.

We provide an exact analytical solution of the Nash equilibrium for $k$- price auctions. We also introduce a new type of auction and demonstrate that it has fair solutions other than the second price auctions, therefore paving the way for replacing second price auctions.

Optimizes bidding strategy for Maker Protocol auctions.

Equity auctions show linear price impact up to a large volume, then non-linear.

The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.

The study compares uniform-price and discriminatory auctions in terms of learning difficulty.

We first investigate the evolution of opening and closing auctions volumes of US equities along the years. We then report dynamical properties of pre-auction periods: the indicative match price is strongly mean-reverting because the imbalance is; the final auction price reacts to a single auction order placement or can…

AHEAD improves financial market efficiency through ad-hoc auctions.

Study on heavy tails in closing auction returns, explaining imbalance through limit order submission.

In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for malicious moneymakers who can commit different types of fraud activities, such as Shill Bidding (SB). The latter is predominant across many a…

Study on efficiency of Dutch auctions on blockchains considering various parameters.

Optimal bidding strategy for multi-platform ad auctions under budget constraints.

Paper addresses selection bias in online advertising auctions.

MiFID II impacts European stock liquidity and price formation.

New algorithm optimizes auction prices in real-time.

Study adapts liquidity model to equity auctions, revealing accelerated event rates and reduced price impact.

Proposes a framework for modeling RTB auctions using point processes.

Paper proposes auction method for smart derivatives to avoid disputes.

We consider a class of auctions (Lowest Unique Bid Auctions) that have achieved a considerable success on the Internet. Bids are made in cents (of euro) and every bidder can bid as many numbers as she wants. The lowest unique bid wins the auction. Every bid has a fixed cost, and once a participant makes a bid, she gets…

We develop extensions to auction theory results that are useful in real life scenarios. 1. Since valuations are generally positive we first develop approximations using the log-normal distribution. This would be useful for many finance related auction settings since asset prices are usually non-negative. 2. We formulat…

Permutation-equivariant neural networks improve auction mechanisms by reducing regret and sample complexity.

Efficient methods estimate bid and value distributions in auctions.

Study learns optimal bidding strategy in auctions with dynamic values and aggregated feedback.

Formal methods verify continuous auctions at exchanges.

We report statistical regularities of the opening and closing auctions of French equities, focusing on the diffusive properties of the indicative auction price. Two mechanisms are at play as the auction end time nears: the typical price change magnitude decreases, favoring underdiffusion, while the rate of these events…

New neural network architecture for auction design exploiting permutation symmetry.

Neural networks can learn optimal auction mechanisms and satisfy mode connectivity.

This study evaluates price improvements in order flow auctions on Ethereum.

We study a phenomenological model for the continuous double auction, equivalent to two independent $M/M/1$ queues. The continuous double auction defines a continuous-time random walk for trade prices. The conditions for ergodicity of the auction are derived and, as a consequence, three possible regimes in the behavior …

Study optimizes rebate design in auction markets to enhance efficiency.