# An Empirical Study of Market Inefficiencies in Uniswap and SushiSwap

arXiv: https://arxiv.org/abs/2203.07774 · HTML: https://mlsanity.com/paper/2203.07774/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning](https://mlsanity.com/paper/2501.07508/) (2025)
- [RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMs](https://mlsanity.com/paper/2602.19419/) (2026)

## Abstract (arXiv)

Decentralized exchanges are revolutionizing finance. With their ever-growing increase in popularity, a natural question that begs to be asked is: how efficient are these new markets? We find that nearly 30% of analyzed trades are executed at an unfavorable rate. Additionally, we observe that, especially during the DeFi summer in 2020, price inaccuracies across the market plagued DEXes. Uniswap and SushiSwap, however, quickly adapt to their increased volumes. We see an increase in market efficiency with time during the observation period. Nonetheless, the DEXes still struggle to track the reference market when cryptocurrency prices are highly volatile. During such periods of high volatility, we observe the market becoming less efficient - manifested by an increased prevalence in cyclic arbitrage opportunities.