Research

In recent years, we have seen the growth of decentralized finance (DeFi), an ecosystem of financial applications and protocols that enable complex, automated, permissionless financial transactions in blockchains (such as Ethereum). We examine decentralized exchanges (DEX), a key DeFi component that facilitates token swaps. DEX prices update continuously and automatically after each swap, creating price shifts for users as their swaps (trades) wait to execute. Users protect themselves from these price shifts by setting a slippage tolerance, which represents the maximum acceptable price increase. This setting is a double-edged sword: lenient tolerance can be exploited through sandwich attacks, which cost the ecosystem over $100 million annually, but stricter tolerance may cause unnecessary failures. We perform a large-scale measurement of the impact of slippage tolerance settings on the health of the Uniswap and Sushiswap DEX ecosystems. To this end, we examine a recent change in Uniswap's default slippage setting, which aimed to mitigate sandwich attacks without increasing the likelihood of transaction failures. This change removed the prior, static default -- 0.5% -- in favor of one dynamically computed for each transaction based on market conditions so that sandwich attacks are less profitable. We find that, overall, Uniswap's new default slippage setting leads to a substantial reduction in Uniswap traders’ losses, approximately 54.7%. The effect is even more pronounced 90% when we only consider traders who followed the default settings. Additionally, we propose some directions for further improving of the default settings. 

Decentralized exchanges' popularity is rising, with liquidity pools widely used for trading. Uniswap V3, a newer version, offers advanced features, but it is more complex to analyze compared to V1 and V2. We compared a simple V2 model's theoretical predictions with Uniswap V3 data. Surprisingly, the V2 model accurately predicted the V3 data in 97.1% of transactions, with a deviation of less than 0.1%. Accuracy was higher in active pools with substantial transaction volume and liquidity, while inactive pools performed less effectively. This approach aids researchers in assessing V2 model suitability for Uniswap V3 data analysis.

The use of decentralized exchange (DEX) platforms has been growing in the last few years. New Layer 2 (L2) blockchain alternatives provide better scalability and lower fees than the Ethereum blockchain (L1), but the security of L2 relative to L1 is unclear and difficult to identify. Using a structural model and a novel and comprehensive data set, we estimate investors’ preferences for blockchain security on two main L2 networks, Polygon and Optimism. We find that traders anticipate an 0.68% (3.29%) chance of losing the transaction value when trading on Polygon (Optimism) compared to L1, and a considerable amount higher than the (0.01%-0.3%) transaction fee charged on each trade. Our work can be seen as empirical evidence of the trade-off between scalability, security, and decentralization, which is the biggest challenge of blockchain networks. 

Decentralized Finance (DeFi) has redefined conventional financial services by enabling intermediary-free peer-to-peer transactions, yielding a wealth of open-source transaction data. In this dynamic DeFi landscape, the emergence of Layer 2 (L2) solutions holds the potential to significantly enhance network efficiency and scalability, surpassing the capabilities of Layer 1 (L1) infrastructure. Nevertheless, the precise impact of L2 solutions remains obscured by the scarcity of transaction data indices that provide meaningful economic insights for empirical analysis. This research endeavors to bridge this critical knowledge gap through a rigorous analysis of raw transactions obtained from Uniswap, a pivotal decentralized exchange (DEX) at the core of the DeFi ecosystem. Our dataset comprises an expansive collection of over 50 million transactions, originating from both Layer 1 (L1) and Layer 2 (L2) networks. Furthermore, we curate a comprehensive repository of daily indices derived from transaction trading data across prominent blockchain networks, including Ethereum, Optimism, Arbitrum, and Polygon. These indices illuminate vital network dynamics, encompassing adoption trends, scalability evaluations, decentralization metrics, wealth distribution profiles, and other pivotal facets within the DeFi landscape. This dataset serves as an invaluable resource, empowering researchers to unravel the intricate relationship between DeFi and Layer 2 solutions, thereby advancing our understanding of this evolving ecosystem.

Basketball games are an important part of college identity and social activities. This paper studies the effect of college basketball game days on the probability of having local sexual assault reports. Using crime data from universities with top basketball programs and local law enforcement agencies, this paper shows that home game days have little effect on the probability of sexual assault reports, while away game days scale up the probability by 14%. This finding is different from those found for football, which likely reflects differences in viewing and partying behavior across the two sports.