Some examples of my current research and working papers can be found below:

Estimating Investor Preferences for Blockchain Security

With Dingyue Liu

Working Paper (Job Market Paper)

Abstract:

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, a considerable amount compared to the (0.01%-0.3%) transaction fee charged on each trade. 

Learning Your Own Risk Preferences

With Gary Charness  &  Dario Trujano-Ochoa

Journal of Risk and Uncertainty

Abstract:

Do people know their own risk preferences, or do risk choices change with experience and observation? We provide a straightforward test in the laboratory. People make an initial decision concerning a lottery choice and then experience 24 unpaid practice periods in which they roll the dice, record the outcome, and record the would-be payoff. They then make a final decision for the lottery choice; one of the first and last periods is randomly chosen for payment. Our primary hypothesis is that people will become less risk-averse by having made and experienced the practice rolls. We do find that people are significantly more likely to become less risk-averse than more risk-averse over time. We note that this move towards assuming increased risk goes in the opposite direction from what is at least arguably predicted by loss aversion and reference dependence. We find that women’s preferences change much less during a session than men’s preferences change. We feel that our literally hands-on approach ensures a degree of engagement that helps to accelerate the learning process. We argue that measures obtained after people have had experience with a mechanism are more meaningful and that this principle might well extend more generally to other elicitation tasks.

Repeated Experience and Consistent Risk Preferences

With Gary Charness

Economics Letters

Economists have developed various methods to elicit risk preferences, which can help forecast decision-making in risky scenarios. However, risk elicitation can be complex, and there remain unresolved challenges. Our research demonstrates that repeated exposure to risk elicitation tasks, such as the Holt-Laury and Eckel-Grossman tasks, results in individuals making more consistent decisions with less noise. This suggests that measuring risk preferences after individuals have gained experience and learning can yield more consistent outcomes and provide a more accurate representation of individual risk preferences. 

The Suitability of Using Uniswap V2 Model to Analyze V3 Data

With Dingyue Liu

Finance Research Letters

Abstract:

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 Power of Default: Measuring the Effect of Slippage Tolerance in Decentralized Exchanges

With Dingyue Liu, Robert McLaughlin,  Nicola Ruaro,  Christopher Kruegel, and Giovanni Vigna 

Accepted at Financial Cryptography and Data Security 2024

Abstract:

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. 

AI Use in Manuscript Preparation for Academic Journals

With Daniel Martin

PLoS One

Abstract:

The emergent abilities of Large Language Models (LLMs), which power tools like ChatGPT and Bard, have produced both excitement and worry about how AI will impact academic writing. In response to rising concerns about AI use, authors of academic publications may decide to voluntarily disclose any AI tools they use to revise their manuscripts, and journals and conferences could begin mandating disclosure and/or turn to using detection services, as many teachers have done with student writing in class settings. Given these looming possibilities, we investigate whether academics view it as necessary to report AI use in manuscript preparation and how detectors react to the use of AI in academic writing.

Uniswap Daily Transactions Indices by Network

With  Lin  William  Cong, Emma Joergensen, Dingyue Liu and Luyao Zhang  

Working Paper

Abstract:

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.

Centralization vs. Decentralization: First Evidence from the Laboratory

With Gabriele Camera and Gary Charness 

Working Paper