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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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13263952 · Jun 202019922001200920172026
48 results for combinatorial auctions

Combinatorial auctions are formulated as frustrated lattice gases on sparse random graphs, allowing the determination of the optimal revenue by methods of statistical physics. Transitions between computationally easy and hard regimes are found and interpreted in terms of the geometric structure of the space of solution…

2006-05-25abs ↗pdf ↗

A fast ML method solves complex combinatorial auction problems.

problem Solving winner determination in multi-unit combinatorial auctions.
method Graph Neural Network (GNN) with half-convolution operations for bid-item graph modeling.
result Approaches optimal performance with negligible revenue loss and low complexity.

CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.

problem Suboptimal search performance due to mismatched database and query distributions.
method CwA jointly learns a balanced database partition and a neural probing function to optimize search performance directly for the query distribution.
result CwA achieves up to 4.7x throughput over state-of-the-art methods at equal recall.

Quantum game theory, whatever opinions may be held due to its abstract physical formalism, have already found various applications even outside the orthodox physics domain. In this paper we introduce the concept of a quantum auction, its advantages and drawbacks. Then we describe the models that have already been put f…

2007-09-26abs ↗pdf ↗

We provide an exact analytical solution of the Nash equilibrium for kk- 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.

2018-09-26abs ↗pdf ↗

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

problem Strategic traders exploit accumulated information to delay their orders, distorting auction efficiency.
method Randomizing auction closing times and designing optimal transaction fees policies.
result Policies encourage strategic traders to send orders earlier, improving auction market efficiency.

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

problem Comparing the learning difficulty of uniform-price and discriminatory multi-unit auctions.
method Characterization of learning difficulty through regret minimization in both full-information and bandit feedback settings.
result Regret scales similarly for both auction formats under full-information, but uniform-price auctions can achieve faster learning rates.

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…

2018-02-06abs ↗pdf ↗

AHEAD improves financial market efficiency through ad-hoc auctions.

problem Improving financial market efficiency and reducing transaction costs.
method Introducing a new matching design (AHEAD) for electronic markets where participants can trade at a fixed price and trigger auctions when unsatisfied.
result A Nash equilibrium is achieved in the market, and ad-hoc auctions are more relevant and efficient than periodic auctions and continuous limit order books.

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

problem Understanding heavy tails in closing auction return distributions.
method Used the stochastic call auction model of Derksen et al. (2020a) to derive and verify a relation between tail exponents.
result Large closing price fluctuations are not caused by large market orders, but by imbalance in limit orders.

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

problem Efficiency and fairness in Dutch auctions on blockchains.
method Modeling Dutch auctions with Poisson process and geometric Brownian motion, computing expected losses and time-to-fill.
result Tradeoff between speed and quality in Dutch auctions, useful for setting parameters.

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

problem Optimizing ad placements for budget-constrained advertisers across multiple platforms.
method Developed an optimal bidding strategy for non-incentive-compatible auctions with budget constraints.
result Maximized total utility across auctions while satisfying budget constraints in expectation.

MiFID II impacts European stock liquidity and price formation.

problem Impact of MiFID II on European stock liquidity and price formation.
method Analyzed effects of MiFID II on European stock markets, focusing on intraday and closing auction liquidity and tick size changes.
result Closing auction volumes increased and price formation became more efficient after MiFID II.

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

problem Understanding and predicting price dynamics in equity auctions.
method Adapted latent/revealed order book framework to equity auctions, measuring order submissions, cancellations, and diffusion rates.
result Equity auctions exhibit accelerated event rates leading to reduced price impact and decreased volatility.

Proposes a framework for modeling RTB auctions using point processes.

problem Modeling and optimizing repeated auctions in the RTB ecosystem.
method Develops a stochastic framework using point processes to model and optimize RTB auctions.
result The proposed framework can be approximated to a Poisson point process, enabling the use of established properties.

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

problem Commitment problem in Ethereum auctions where builders can defect and replicate winning MEV opportunities.
method Modeling and analysis of searchers' bidding strategies and the resulting equilibrium, using libMEV dataset.
result The equilibrium is piecewise, with the cost of imperfect commitment depending on replicability and competition. There is sharp heterogeneity across MEV types.

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…

2010-07-24abs ↗pdf ↗

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

problem Optimizing reserve prices for first-price auctions in display advertising.
method Gradient-based algorithm to adaptively update and optimize reserve prices based on bidder responsiveness to experimental shocks.
result Revenue optimization in first-price auctions can be decomposed into demand and bidding components, and techniques are introduced to reduce variance of each.

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…

2018-09-25abs ↗pdf ↗

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

problem Learning to bid optimally in repeated first-price auctions with incomplete feedback.
method Developed an algorithm exploiting the specific feedback structure and payoff function of first-price auctions.
result Achieved a near-optimal O~(T)\widetilde{O}(\sqrt{T}) regret bound for first-price auctions.

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

problem Designing optimal auction mechanisms that balance revenue and bidders' regret.
method Introduced permutation-equivariant neural networks to auction mechanisms.
result Permutation-equivariant neural networks decrease expected ex-post regret and improve model generalizability.

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

problem Optimizing bidding in auctions with time-dependent values and limited feedback.
method Combines plug-in estimators with differential-equation characterization of optimal policy.
result Achieves near optimal regret bounds for learning optimal policy.

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

problem How to bid optimally and efficiently in adversarial first-price auctions.
method Developed a minimax optimal online bidding algorithm leveraging expert-chaining structure and exploiting product structure.
result Achieved an O~(T)\widetilde{O}(\sqrt{T}) regret, superior to existing algorithms.

Study optimal auction formats for maximizing MEV on Ethereum.

problem Maximizing extractable value from Ethereum auctions.
method Empirical analysis of 2.2 million transactions, modeling affiliation among bidders.
result English and second-price sealed-bid auctions dominate other formats, with significant revenue losses.

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

problem Improving trading outcomes in blockchain-based trading platforms.
method Utilized open-source tools to attribute price improvements to specific system inputs.
result Auction-enhanced interfaces can provide statistically significant improvements in trading outcomes, averaging 4-5 basis points.

We study a phenomenological model for the continuous double auction, equivalent to two independent M/M/1M/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 …

2013-05-13abs ↗pdf ↗

Study optimizes rebate design in auction markets to enhance efficiency.

problem Designing optimal rebate policies in auction markets to improve efficiency.
method Formulated as a principal-agent problem, solved using Hamilton-Jacobi-Bellman equations and Deep BSDE method.
result Optimal transaction fees and rebates narrow the price spread, improving market efficiency.

Efficient verified double auctions improve matching speed and detect errors.

problem Improving the efficiency and reliability of double auctions in financial markets.
method Formally verified implementation using Coq proof assistant, reducing time complexity and improving error detection.
result Improved efficiency with O(nlogn)O(n \log n) time complexity, reducing runtime from days to minutes.

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 …

2013-12-10abs ↗pdf ↗

COAD maximizes online auction revenue by quantifying uncertainty without known distributions.

problem Designing incentive-compatible mechanisms for online auctions with unknown bidder values and uncertain future participants.
method COAD uses distribution-free uncertainty quantification techniques and integrates machine learning methods to predict bidder values while ensuring revenue guarantees.
result COAD maximizes revenue in online auctions through bidder-specific reserve prices based on lower confidence bounds of valuations.

Optimizes bidding in hourly and quarter-hourly electricity markets to reduce price impact.

problem Maximizing profit in two consecutive electricity markets with market impact and transaction costs.
method Examined multiple price scenarios, estimated market impact, used trading strategies, provided theoretical results.
result Minimizing price impact is more profitable than maximizing arbitrage in the German EPEX market.