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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.

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48 results for auction mechanisms

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.

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.

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.

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.

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.

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.

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 ↗

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.

New neural network architecture for auction design exploiting permutation symmetry.

problem Designing incentive-compatible auctions that maximize expected revenue.
method Constructed a permutation-equivariant neural network architecture.
result Permutation-equivariant architectures can perfectly recover optimal mechanisms.

Improved εε-greedy handles strategic bidding in PPC auctions.

problem Strategic bidding in PPC auctions with personalization and corruptions.
method Extended εε-greedy to handle strategic arms in contextual multi-arm bandit.
result εε-greedy is robust to adversarial corruptions and degrades linearly with corruption.

New auction design uses statistical learning to reduce costs and improve fairness.

problem Designing efficient multi-item auctions with reduced implementation costs and fairness.
method Nonparametric density estimation for credible intervals, two new strategies.
result Strategies consistently outperform alternative methods in revenue maximization and cost reduction.

A Deep Q-Learning framework tackles market-making by incorporating closing auctions.

problem Managing end-of-day risk in market-making models.
method Developed a Deep Q-Learning framework that anticipates closing auctions and continuously refines projected clearing prices.
result The Deep Q-Learning framework outperforms classical market-making models in simulations and real data.

Real-time bidding (RTB) systems, which utilize auctions to allocate user impressions to competing advertisers, continue to enjoy success in digital advertising. Assessing the effectiveness of such advertising remains a challenge in research and practice. This paper proposes a new approach to perform causal inference on…

2019-08-22abs ↗pdf ↗

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.

The call auction is a widely used trading mechanism, especially during the opening and closing periods of financial markets. In this paper, we study a standard call auction problem where orders are submitted according to Poisson processes, with random prices distributed according to a general distribution, and may be c…

2014-07-16abs ↗pdf ↗

We derive valuations of a portfolio of financial instruments from a securities lending perspective, under different assumptions, and show a weighting scheme that converges to the true valuation. We illustrate conditions under which our alternative weighting scheme converges faster to the true valuation when compared to…

2016-03-03abs ↗pdf ↗

Study optimal bidding strategies for digital ads targeting purchases and health campaigns.

problem Optimizing advertising strategies in digital channels.
method Continuous-time models encoding user behavior and auction mechanisms, semi-explicit formulas for optimal bidding.
result Semi-explicit formulas for optimal value and bidding policy for different types of advertising.

Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism…

2018-12-13abs ↗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 ↗

A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.

problem Optimizing reserve prices in multi-phase second-price auctions with noisy and potentially untruthful bidders.
method Combines RL techniques with buffer periods, a novel algorithm, and LSVI-UCB extension.
result Achieves optimal revenue regret under known and unknown noise conditions.

Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.

problem Estimating long-term treatment effects in online advertising systems.
method Modeling treatment effects as a stopped random walk, using experimental design, and statistical methods.
result Confidence intervals for long-term treatment effects constructed using statistical techniques.

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 ↗

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.

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.