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.

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.

Trading on decentralized exchanges via an Automated Market Maker (AMM) mechanism has been massively adopted, with a daily trading volume reaching $1B. This trading method has also received close attention from researchers, central banks, and financial firms, who have the potential to adopt it to traditional financial markets such as foreign exchanges and stock markets. A critical challenge of AMM-powered trading is that transaction order has high financial value, so a policy or method to order transactions in a “good” (optimal) manner is vital. We offer economic measures of both price stability (low volatility) and inequality that inform how a “social planner” should pick an optimal ordering. We show that there is a trade-off between achieving price stability and reducing inequality, and that policymakers must choose which to prioritize. In addition, picking the optimal order can often be costly, especially when performing an exhaustive search over trade orderings (permutations). As an alternative we provide a simple algorithm, Clever Lookahead Volatility Reduction (CLVR). This algorithm constructs an ordering which approximately minimizes price volatility with a small computation cost. We also provide insight into the strategy changes that may occur if traders are subject to this sequencing algorithm.

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. 

We empirically study liquidity and market depth on decentralized exchanges (DEXs), identifying factors at the blockchain, token pair, and pool levels that predict future effective spreads for fixed trade sizes on Uniswap v3 pools. Introducing the v2 counterfactual spread metric, a novel criterion that assesses the degree of liquidity concentration in pools using the "concentrated liquidity" mechanism, we decompose the effect of each factor on market depth into two channels: total value locked (TVL) and concentration. We further explore how external liquidity from competing DEXs and fillers with private inventory on DEX aggregators influence market depth. We find, for moderately-sized swaps, that (i) gas prices, returns, and volatility affect spreads primarily through concentration, (ii) internalization of swaps by private liquidity sources affects spreads primarily through TVL, and (iii) fee revenue, markout, and DEX competition affect spreads through both channels.

Procrastination is a common occurrence in everyday life, particularly among students. In this paper, we explore the implementation of a gamified leaderboard within an undergraduate economics course to assess its impact on class engagement and procrastination reduction. The leaderboard is integrated within weekly online assignments, auto-graded using an AI-assisted platform. Students achieving a full score and submitting their work earlier are ranked higher on the leaderboard. Our results suggest that the treated group, i.e., the group exposed to the leaderboard, exhibits earlier completion times relative to the control group. i.e., the group not exposed to the leaderboard. This points to the positive influence of gamified leaderboards on reducing procrastination tendencies and motivating students to complete tasks earlier.

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.