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Claudio Tessone

Publications and source records attributed to Claudio Tessone.

At least 19 recordsLinked to original sources

How to Serve Your Sandwich? MEV Attacks in Private L2 Mempools

We study the feasibility, profitability, and prevalence of sandwich attacks on Ethereum rollups with private mempools. First, we extend a formal model of optimal front- and back-run sizing, relating attack profitability to victim trade volume, liquidity depth, and slippage bounds. We complement it with an execution-feasibility model that quantifies co-inclusion constraints under private mempools. Second, we examine execution constraints in the absence of builder markets: without guaranteed atomic inclusion, attackers must rely on sequencer ordering, redundant submissions, and priority fee placement, which renders sandwiching probabilistic rather than deterministic. Third, using transaction-level data from major rollups, we show that naive heuristics overstate sandwich activity. We find that the majority of flagged patterns are false positives and that the median net return for these attacks is negative. Our results suggest that sandwiching, while endemic and profitable on Ethereum L1, is rare, unprofitable, and largely absent in rollups with private mempools. These findings challenge prevailing assumptions, refine measurement of MEV in L2s, and inform the design of sequencing policies.

cs.CR

When Priority Fails: Revert-Based MEV on Fast-Finality Rollups

We study the economics of transaction reverts on Ethereum rollups and show that they are not accidental failures but equilibrium outcomes of MEV strategies. Using execution traces from major L2s, we find that over 80% of reverted transactions are swaps, with half targeting USDC-WETH pools on Uniswap v3, v4. Clustering reveals distinct bot archetypes, including split-trade arbitrageurs, atomic duplicators, and end-of-block spammers, demonstrating that reverts follow systematic patterns rather than random noise. Empirically, we show that priority fee auctions on rollups do not allocate blockspace efficiently: transaction placement is mis-ordered, round-number bidding dominates, and duplication spam inflates base fees. As a result, reverted transactions contribute disproportionately more to sequencer fee revenues than to gas consumption, shifting welfare from users to sequencers. To explain these dynamics, we develop a model proving that trade-splitting and duplication strictly dominate one-shot execution under convex adversarial loss. Our findings establish reverts as a structural feature of rollup MEV microstructure and highlight the need for protocol-level reforms to sequencing, fee markets, and revert protection.

cs.CR

Scaling DeFi with ZK Rollups: Design, Deployment, and Evaluation of a Real-Time Proof-of-Concept

Ethereum's scalability limitations pose significant challenges for the adoption of decentralized applications (dApps). Zero-Knowledge Rollups (ZK Rollups) present a promising solution, bundling transactions off-chain and submitting validity proofs on-chain to enhance throughput and efficiency. In this work, we examine the technical underpinnings of ZK Rollups and stress test their performance in real-world applications in decentralized finance (DeFi). We set up a proof-of-concept (PoC) consisting of ZK rollup and decentralized exchange, and implement load balancer generating token swaps. Our results show that the rollup can process up to 71 swap transactions per second, compared to 12 general transaction by Ethereum. We further analyze transaction finality trade-offs with related security concerns, and discuss the future directions for integrating ZK Rollups into Ethereum's broader ecosystem.

cs.CR

From Rules to Rewards: Reinforcement Learning for Interest Rate Adjustment in DeFi Lending

Decentralized Finance (DeFi) lending enables permissionless borrowing via smart contracts. However, it faces challenges in optimizing interest rates, mitigating bad debt, and improving capital efficiency. Rule-based interest-rate models struggle to adapt to dynamic market conditions, leading to inefficiencies. This work applies Offline Reinforcement Learning (RL) to optimize interest rate adjustments in DeFi lending protocols. Using historical data from Aave protocol, we evaluate three RL approaches: Conservative Q-Learning (CQL), Behavior Cloning (BC), and TD3 with Behavior Cloning (TD3-BC). TD3-BC demonstrates superior performance in balancing utilization, capital stability, and risk, outperforming existing models. It adapts effectively to historical stress events like the May 2021 crash and the March 2023 USDC depeg, showcasing potential for automated, real-time governance.

cs.LG

A Line Graph-Based Framework for Identifying Optimal Routing Paths in Decentralized Exchanges

Decentralized exchanges, such as those employing constant product market makers (CPMMs) like Uniswap V2, play a crucial role in the blockchain ecosystem by enabling peer-to-peer token swaps without intermediaries. Despite the increasing volume of transactions, there remains limited research on identifying optimal trading paths across multiple DEXs. This paper presents a novel line-graph-based algorithm (LG) designed to efficiently discover profitable trading routes within DEX environments. We benchmark LG against the widely adopted Depth-First Search (DFS) algorithm under a linear routing scenario, encompassing platforms such as Uniswap, SushiSwap, and PancakeSwap. Experimental results demonstrate that LG consistently identifies trading paths that are as profitable as, or more profitable than, those found by DFS, while incurring comparable gas costs. Evaluations on Uniswap V2 token graphs across two temporal snapshots further validate LG's performance. Although LG exhibits exponential runtime growth with respect to graph size in empirical tests, it remains viable for practical, real-world use cases. Our findings underscore the potential of the LG algorithm for industrial adoption, offering tangible benefits to traders and market participants in the DeFi space.

q-fin.CP

Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks

Cryptocurrencies are digital tokens built on blockchain technology, with thousands actively traded on centralized exchanges (CEXs). Unlike stocks, which are backed by real businesses, cryptocurrencies are recognized as a distinct class of assets by researchers. How do investors treat this new category of asset in trading? Are they similar to stocks as an investment tool for investors? We answer these questions by investigating cryptocurrencies' and stocks' price time series which can reflect investors' attitudes towards the targeted assets. Concretely, we use different machine learning models to classify cryptocurrencies' and stocks' price time series in the same period and get an extremely high accuracy rate, which reflects that cryptocurrency investors behave differently in trading from stock investors. We then extract features from these price time series to explain the price pattern difference, including mean, variance, maximum, minimum, kurtosis, skewness, and first to third-order autocorrelation, etc., and then use machine learning methods including logistic regression (LR), random forest (RF), support vector machine (SVM), etc. for classification. The classification results show that these extracted features can help to explain the price time series pattern difference between cryptocurrencies and stocks.

q-fin.ST

Machine Learning-Based Detection of Pump-and-Dump Schemes in Real-Time

Cryptocurrency markets often face manipulation through prevalent pump-and-dump (P&D) schemes, where self-organized Telegram groups, some exceeding two million members, artificially inflate target cryptocurrency prices. These groups sell premium access to inside information, worsening information asymmetry and financial risks for subscribers and all investors. This paper presents a real-time prediction pipeline to forecast target coins and alert investors to possible P&D schemes. In a Poloniex case study, the model accurately identified the target coin among the top five from 50 random coins in 24 out of 43 (55.81%) P&D events. The pipeline uses advanced natural language processing (NLP) to classify Telegram messages, identifying 2,079 past pump events and detecting new ones in real-time.

cs.CE

Liquidity Fragmentation or Optimization? Analyzing Automated Market Makers Across Ethereum and Rollups

Layer-2 (L2) blockchains inherit Ethereums security guarantees while reducing gas fees. As a result, they are gaining traction among traders at Automated Market Makers (AMMs), sparking debate over whether they contribute to liquidity fragmentation of Ethereum. Our research suggests that such fragmentation is not currently occurring. However, it could emerge in the future, particularly if Liquidity Providers (LPs) recognize the higher returns available on L2s. Using Lagrangian optimization, we develop a model for optimal liquidity allocation across AMMs on Ethereum and its L2s, using staking as a benchmark. We show that, in equilibrium, AMM liquidity provision returns converge to this reference rate. Additionally, we measure the elasticity of trading volume with respect to Total Value Locked (TVL) in AMMs and find that, on well-established blockchains, an increase in TVL does not necessarily lead to higher trading volume. Finally, our empirical findings reveal that Ethereums liquidity pools are oversubscribed compared to those on L2s and often yield lower returns than staking Ether. LPs could maximize their rewards by reallocating more than two-thirds of their liquidity to L2s and staking.

cs.CE

Bitcoin Transaction Behavior Modeling Based on Balance Data

When analyzing Bitcoin users' balance distribution, we observed that it follows a log-normal pattern. Drawing parallels from the successful application of Gibrat's law of proportional growth in explaining city size and word frequency distributions, we tested whether the same principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from one. This suggests that Gibrat's proportional growth rule alone does not fully explain the log-normal distribution observed in Bitcoin users' balances. During our exploration, we discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their behavior, which we refer to as ``poor" and ``wealthy" users. Poor users, who initially purchase only a small amount of Bitcoin, tend to buy more bitcoins first and then sell out all their holdings gradually over time. The certainty of selling all their coins is higher and higher with time. In contrast, wealthy users, who acquire a large amount of Bitcoin from the start, tend to sell off their holdings over time. The speed at which they sell their bitcoins is lower and lower over time and they will hold at least a small part of their initial holdings at last. Interestingly, the wealthier the user, the larger the proportion of their balance and the higher the certainty they tend to sell. This research provided an interesting perspective to explore bitcoin users' behaviors which may apply to other finance markets.

econ.GN

An Improved Algorithm to Identify More Arbitrage Opportunities on Decentralized Exchanges

In decentralized exchanges (DEXs), the arbitrage paths exist abundantly in the form of both arbitrage loops (e.g. the arbitrage path starts from token A and back to token A again in the end, A, B,..., A) and non-loops (e.g. the arbitrage path starts from token A and stops at a different token N, A, B,..., N). The Moore-Bellman-Ford algorithm, often coupled with the ``walk to the root" technique, is commonly employed for detecting arbitrage loops in the token graph of decentralized exchanges (DEXs) such as Uniswap. However, a limitation of this algorithm is its ability to recognize only a limited number of arbitrage loops in each run. Additionally, it cannot specify the starting token of the detected arbitrage loops, further constraining its effectiveness in certain scenarios. Another limitation of this algorithm is its incapacity to detect non-loop arbitrage paths between any specified pairs of tokens. In this paper, we develop a new method to solve these problems by combining the line graph and a modified Moore-Bellman-Ford algorithm (MMBF). This method can help to find more arbitrage loops by detecting at least one arbitrage loop starting from any specified tokens in the DEXs and can detect the non-loop arbitrage paths between any pair of tokens. Then, we applied our algorithm to Uniswap V2 and found more arbitrage loops and non-loops indeed compared with applying the Moore-Bellman-Ford (MBF) combined algorithm. The found arbitrage profit by our method in some arbitrage paths can be even as high as one million dollars, far larger than that found by the MBF combined algorithm. Finally, we statistically compare the distribution of arbitrage path lengths and the arbitrage profit detected by both our method and the MBF combined algorithm, and depict how potential arbitrage opportunities change with time by our method.

q-fin.CP

Velocity, Holding Time and Lifespan of Cryptocurrency in Transactions

The measurement of the velocity of money is still a significant topic. In this paper, we proposed a method to calculate the velocity of money by combining the holding-time distribution and lifespan distribution. By derivation, the velocity of money equals the holding-time distribution's value at zero. When we have much holding-time data, this problem can be converted to a regression problem. After a numeric simulation, we find that the calculating accuracy is high even if we used only a small part of the holding time data, which implies a potential application in measuring the velocity of money in reality, such as digital money. We also tested the methods on Cardano and found that the method can also provide a reasonable estimation of velocity in some cases.

econ.GN

Profit Maximization In Arbitrage Loops

Cyclic arbitrage chances exist abundantly among decentralized exchanges (DEXs), like Uniswap V2. For an arbitrage cycle (loop), researchers or practitioners usually choose a specific token, such as Ether as input, and optimize their input amount to get the net maximal amount of the specific token as arbitrage profit. By considering the tokens' prices from CEXs in this paper, the new arbitrage profit, called monetized arbitrage profit, will be quantified as the product of the net number of a specific token we got from the arbitrage loop and its corresponding price in CEXs. Based on this concept, we put forward three different strategies to maximize the monetized arbitrage profit for each arbitrage loop. The first strategy is called the MaxPrice strategy. Under this strategy, arbitrageurs start arbitrage only from the token with the highest CEX price. The second strategy is called the MaxMax strategy. Under this strategy, we calculate the monetized arbitrage profit for each token as input in turn in the arbitrage loop. Then, we pick up the most maximal monetized arbitrage profit among them as the monetized arbitrage profit of the MaxMax strategy. The third one is called the Convex Optimization strategy. By mapping the MaxMax strategy to a convex optimization problem, we proved that the Convex Optimization strategy could get more profit in theory than the MaxMax strategy, which is proved again in a given example. We also proved that if no arbitrage profit exists according to the MaxMax strategy, then the Convex Optimization strategy can not detect any arbitrage profit, either. However, the empirical data analysis denotes that the profitability of the Convex Optimization strategy is almost equal to that of the MaxMax strategy, and the MaxPrice strategy is not reliable in getting the maximal monetized arbitrage profit compared to the MaxMax strategy.

q-fin.CP

Cross-Rollup MEV: Non-Atomic Arbitrage Across L2 Blockchains

This study quantifies the potential non-atomic MEV on Layer-2 (L2) blockchains by measuring the arbitrage opportunities between cross-rollup and DEX-CEX. Over recent years, we observe a shift in trading activities from Ethereum to rollups, with swaps on rollups occurring 2-3 times more frequently, albeit with lower trade volumes. By analyzing the costs of swap on L2s and price discrepancies cross-rollup and DEX-CEX, we identify more than 500 000 unexplored arbitrage opportunities. In particular, we find that these opportunities persist, on average, for 10 to 20 blocks, necessitating the modification of the Loss Versus Rebalancing (LVR) metric to prevent double-counting. Our findings indicate that the arbitrage opportunities in Arbitrum, Base, and Optimism range between 0.03% and 0.05% of the trading volume, while in the ZKsync it fluctuates around 0.25%.

cs.CR

SoK: Decentralized Finance (DeFi) -- Fundamentals, Taxonomy and Risks

Decentralized Finance (DeFi) refers to financial services that are not necessarily related to crypto-currencies. By employing blockchain for security and integrity, DeFi creates new possibilities that attract retail and institution users, including central banks. Given its novel applications and sophisticated designs, the distinction between DeFi services and understanding the risk involved is often complex. This work systematically presents the major categories of DeFi protocols that cover over 90\% of total value locked (TVL) in DeFi. It establishes a structured methodology to differentiate between DeFi protocols based on their design and architecture. Every DeFi protocol is classified into one of three groups: liquidity pools, pegged and synthetic tokens, and aggregator protocols, followed by risk analysis. In particular, we classify stablecoins, liquid staking tokens, and bridged (wrapped) assets as pegged tokens resembling similar risks. The full risk exposure of DeFi users is derived not only from the DeFi protocol design but also from how it is used and with which tokens.

cs.CR

SoK: Liquid Staking Tokens (LSTs) and Emerging Trends in Restaking

Liquid staking and restaking represent recent innovations in Decentralized Finance (DeFi) that garnered user interest and capital. Liquid Staking Tokens (LSTs), tokenized representations of staked tokens on Proof-of-Stake (PoS) blockchains, are the leading staking method. LSTs offer users the ability to earn staking rewards while maintaining liquidity, enabling seamless integration into DeFi protocols and free tradeability. Restaking builds upon this concept by allowing staked tokens, LSTs or native Bitcoin tokens to secure additional protocols and PoS chains for supplementary rewards. Liquid Restaking Tokens (LRTs) unlock liquidity of restaked assets. This Systematization of Knowledge (SoK) establishes a comprehensive framework for the technical and economic models of liquid staking protocols. Using this framework, we systematically compare protocols mechanics, including node operator selection, staking reward distribution, and slashing. Our empirical analysis of token performance reveals that protocol design and market dynamics impact token market value. We further present the recent developments in restaking and discuss associated risks and security implications. Lastly, we review the emerging literature on liquid staking and restaking.

cs.CR

Quantifying Arbitrage in Automated Market Makers: An Empirical Study of Ethereum ZK Rollups

Arbitrage can arise from the simultaneous purchase and sale of the same asset in different markets in order to profit from a difference in its price. This work systematically reviews arbitrage opportunities between Automated Market Makers (AMMs) on Ethereum ZK rollups, and Centralised Exchanges (CEXs). First, we propose a theoretical framework to measure such arbitrage opportunities and derive a formula for the related Maximal Arbitrage Value (MAV) that accounts for both price divergences and liquidity available in the trading venues. Then, we empirically measure the historical MAV available between SyncSwap, an AMM on zkSync Era, and Binance, and investigate how quickly misalignments in price are corrected against explicit and implicit market costs. Overall, the cumulative MAV from July to September 2023 on the USDC-ETH SyncSwap pool amounts to $104.96k (0.24% of trading volume).

cs.CR

Liquid Staking Tokens in Automated Market Makers

This paper studies liquid staking tokens (LSTs) on automated market makers (AMMs), both theoretically and empirically. LSTs are tokenized representations of staked assets on proof-of-stake blockchains. First, we model LST-liquidity on AMMs theoretically, categorizing suitable AMM types for LST liquidity and deriving formulas for the necessary returns from trading fees to adequately compensate liquidity providers under the particular price trajectories of LSTs. For the latter, two relevant metrics are considered: (1) losses compared to holding the liquidity outside the AMM (loss-versus-holding, or "impermanent loss"), and (2) the relative profitability compared to fully staking the capital (loss-versus-staking) which is specifically tailored to the case of LST-liquidity. Next, we empirically measure these metrics for Ethereum LSTs across the most relevant AMM pools. We find that, while trading fees often compensate for impermanent loss, fully staking is more profitable for many pools, raising questions about the sustainability of the current LST liquidity allocation to AMMs.

cs.CR

Empirical and Theoretical Analysis of Liquid Staking Protocols

Liquid staking has become the largest category of decentralized finance protocols in terms of total value locked. However, few studies exist on its implementation designs or underlying risks. The liquid staking protocols allow for earning staking rewards without the disadvantage of locking the capital at the validators. Yet, they are seen by some as a threat to the Proof-of-Stake blockchain security. This paper is the first work that classifies liquid staking implementations. It analyzes the historical performance of major liquid staking tokens in comparison to the traditional staking for the largest Proof-of-Stake blockchains. Furthermore, the research investigates the impact of centralization, maximum extractable value and the migration of Ethereum from Proof-of-Work to Proof-of-Stake on the tokens' performance. Examining the tracking error of the liquid stacking providers to the staking rewards shows that they are persistent and cannot be explained by macro-variables of the currency, such as the variance or return.

cs.CR