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.
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.
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.
Research examines how strategic latency manipulation impacts Ethereum's network efficiency and decentralization.
problem Impact of artificial latency on Ethereum's network efficiency and decentralization.
method Comprehensive analysis of MEV-Boost auction system and empirical validation with a pilot.
result Increased profitability for node operators and significant systemic challenges like heightened network inefficiencies and centralization risks.
The study examines how backrun auctions can protect traders from price manipulation.
problem Price manipulation by arbitrageurs in batched trading venues.
method Developed a laminated queueing model to study price manipulation and introduced a price manipulation coefficient.
result Bound the price manipulation coefficient and found it approximated by a 'zeta value' with measurable parameters.
Non-atomic arbitrage exploits price differences on Ethereum and other blockchains, accounting for over 10% of Ethereum's block value.
problem Price differences on decentralized exchanges and centralized exchanges lead to MEV.
method Analyzed non-atomic arbitrage on Ethereum's largest DEXes, identifying its prevalence and impact.
result More than 10% of Ethereum's block value is attributed to non-atomic arbitrage, involving over $132 billion.
Ethereum tackles bribery in blockchain transactions with new fee mechanism.
problem Bribing miners in Ethereum blockchain to manipulate transaction fees.
method Filtered transactions, constructed proxies for bribery level, analyzed effects on blockchain and financial markets.
result Bribing affects Ethereum and other blockchains, influencing cryptocurrency, transaction stats, and network adoption.
The paper assesses dimensionality reduction for cryptocurrency link prediction.
problem Establishing a link between cryptocurrencies using dimensionality reduction techniques.
method Used canonical correlation analysis and principal component analysis on log returns and covariates of Bitcoin and Ethereum.
result Performance of dimensionality reduction techniques in forecasting Ethereum returns with Bitcoin features.
We provide an exact analytical solution of the Nash equilibrium for k k 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.
Study compares Bitcoin and Ethereum tail behavior using Q-Q plots.
problem Examining tail risk in cryptocurrency returns.
method Used Q-Q plots and Generalized Tempered Stable (GTS) distribution.
result Ethereum shows more extreme values than Bitcoin, indicating greater tail risk.
Optimizes bidding strategy for Maker Protocol auctions.
problem Minimizing costs in Maker Protocol auctions.
method Developed and optimized a bidding function using historical data.
result Most auctions end at higher prices than optimal recommendations.
Equity auctions show linear price impact up to a large volume, then non-linear.
problem Understanding price impact in equity auctions.
method High-quality data analysis of Paris stock exchange auctions.
result Price impact is linear up to a large volume, then becomes non-linear.
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.
This paper analyzes Ethereum's gas fees and their derivatives, providing a comprehensive model.
problem Understanding and predicting gas fees on the Ethereum blockchain.
method Analyzed Ethereum's gas fee structure and used a fractional Ornstein-Uhlenbeck process to model gas prices.
result A model for pricing and trading gas fee derivatives to hedge against volatility.
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.
Ethereum transition to PoS reduces energy consumption and decentralizes the network.
problem Transitioning from proof-of-work to proof-of-stake to reduce energy consumption and decentralize the network.
method Analyzed the impact of the Ethereum transition to proof-of-stake on network performance, competing platforms, and transaction fees.
result The transition to PoS has reduced energy consumption by 99.98% and decreased network concentration.
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…
Ethereum trends analyzed through blockchain transactions and Google searches.
problem Identifying market manipulation in crypto prices.
method Big data analysis of Ethereum transactions, smart contracts, and search volumes.
result Big players manipulate crypto markets after price drops.
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 proposes a machine learning method for bid shading in first-price auctions.
problem Maintaining strategy equilibrium in first-price auctions.
method Machine learning approach to model optimal bid shading.
result Demonstrates superiority and robustness of new approach across various metrics.
Transformer predicts Ethereum prices using cross-currency correlation and sentiment analysis.
problem Predicting Ethereum cryptocurrency prices with limited data.
method Transformer-based neural network with cross-currency correlation and sentiment analysis.
result Transformer model outperforms other models on some parameters.
Panoptic trades options without oracles on Ethereum.
problem Trading options without relying on oracles.
method Perpetual, trustless, instant-settlement protocol on Ethereum.
result Trustless, permissionless trading of options on Uniswap v3.
Maker Protocol manages Dai stable coin on Ethereum blockchain.
problem Managing decentralized finance applications on blockchain.
method Analyzes Maker Protocol's components and governance.
result Maker Protocol is a significant decentralized finance application.
Faster Ethereum slots boost CEX-DEX arbitrage by 535% and 203%.
problem Reducing Ethereum slot time impacts CEX-DEX arbitrage opportunities.
method Developed a trading model to simulate and compare agent behavior under different slot times.
result Faster slot times increase CEX-DEX arbitrage activity and returns.
Ethereum upgrades increased TPS and lowered fees, with L2s surpassing Solana in 2029.
problem Transaction speed and fees in Ethereum
method Comparing Ethereum Mainnet and Layer 2 networks, Solana, and Polygon
result Ethereum Mainnet and L2 networks surpassed Solana in terms of TPS and lowered fees
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.
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.
Ethereum block builders can earn up to $14M/month by reordering transactions, harming users.
problem Block builders can exploit transaction reordering to earn significant profits, harming users.
method Estimation of MEV payments and analysis of reordering effects.
result Block builders can earn up to $14M/month by reordering transactions, skewing the distribution.
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.
Paper addresses selection bias in online advertising auctions.
problem Selection bias affects auction truthfulness and advertiser profits.
method Theoretical analysis combined with multi-task learning.
result Selection bias can be significantly reduced using multi-task learning.
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 examines revenue from scam tokens on Ethereum, revealing key characteristics and market factors.
problem Revenue from scam tokens on Ethereum blockchain.
method Empirical analysis of Uniswap, examining characteristics and market factors.
result Revenue from scam tokens is influenced by market economic factors and community engagement.
New algorithm optimizes auction prices in real-time.
problem Maximizing revenue in online auctions with high frequency data.
method First real-time algorithm for online learning of monopoly prices.
result Achieves constant time and memory complexity for updates.
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.
SVAR-LiNGAM reveals causal order in crypto-asset markets.
problem Understanding the causal relationships between spot rates and crypto-assets.
method Applied SVAR-LiNGAM to analyze spot exchange rates and crypto-asset exchange rates.
result Causal order found: EUR_USD spot rate -> Bitcoin -> Ethereum -> Ripple.
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.
Paper proposes auction method for smart derivatives to avoid disputes.
problem Disputes over derivative liquidation processes in smart contracts.
method Defines an auction type resolution for smart derivatives.
result Proposes a beneficial method for smart derivatives participants.
TLMG4Eth combines language and graph models for Ethereum fraud detection.
problem Current fraud detection methods fail to consider semantic and similarity patterns in Ethereum transactions.
method TLMG4Eth uses a transaction language model and graph-based methods to capture semantic, similarity, and structural features.
result TLMG4Eth detects anomalies in Ethereum transactions more effectively than existing methods.
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…
Enhances Ponzi scheme detection on Ethereum using time-aware metapaths.
problem Lack of temporal information in heterogeneous transaction graphs.
method Time-aware Metapath Feature Augmentation (TMFAug) module.
result Significant performance improvements in Ponzi scheme detection.
Deep network optimizes ad bidding for first-price auctions.
problem Optimizing bid prices for first-price auctions in online advertising.
method Introduced a deep distribution network for optimal bidding.
result Algorithm outperforms previous methods in terms of surplus and eCPX metrics.
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…
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}) O ( 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.
Efficient methods estimate bid and value distributions in auctions.
problem Estimating bid and value distributions in auctions with limited information.
method Non-parametric estimation algorithms for first- and second-price auctions.
result Uniform estimation bounds for bid and value distributions, independent of distributions being estimated.
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.