Study Finds 167.17 USD Maximum Revenue From MEV Arbitrage On Algorand

An empirical study on time-constrained arbitrage has been conducted to assess the presence of Miner Extractable Value (MEV) on the Algorand blockchain. The study focuses on developing an algorithm to detect profitable arbitrage opportunities within the specific time constraints of Algorand’s First-Come-First-Served (FCFS) network. The research builds on prior work by Zhou et al. on Ethereum, which used a greedy method for real-time cyclic arbitrage detection. The study aims to identify nearly all emerging arbitrage cycles and incorporate a more efficient input optimization technique tailored to Algorand’s environment.

The algorithm was tested in both unconstrained and time-constrained settings, with the latter being more relevant for the competitive dynamics of an FCFS network. The study collected state data from Algorand and executed the algorithm subsequent to each block’s finality. This approach allowed for an assessment of the algorithm’s practicality and efficiency in a real-world blockchain environment. The algorithm operates within a predefined time window τ, which depends on the arrival time of the first transaction changing a relevant pool’s state. In FCFS networks, the desired position in a block can only be achieved by correctly timing the transaction issuance and propagation to the network.

The study faced certain constraints, including the lack of a forking tool on Algorand, which limited the ability to construct the blockchain state at a desired block and execute a transaction. This limitation led to a focus on a subset of assets, pools, and DEX protocols. The study also simplified financial modeling by disregarding flash loan fees and assuming the immediate availability of initial assets, which may not reflect real-world trading conditions. Despite these limitations, the study provides valuable insights into the efficiency of MEV searchers on Algorand and the potential for further optimization of arbitrage strategies.

The study tracked 136 assets exchanged in 255 pools on three different DEXs over a 16-day period. In 7.1% of the 430,996 blocks built during this period, the reserves of at least one pool were updated, and the arbitrage detection algorithm was run on it. The results showed that MEV searchers on Algorand promptly exploit arbitrage opportunities within the block they emerge, with the maximum realized revenue of an arbitrageur being 167.17 USD. The study also found that the efficiency of MEV searchers on Algorand showcases that searching for MEV only at the block state level is a naive approach. Future work will focus on collecting transaction data on the mempool directly and executing the algorithm after every trade on a pool relevant to the study.

The study also measured the profitability of the algorithm as a function of τ to observe the impact of the available runtime window on the discovered value. The results indicated that the discovered arbitrage revenue only significantly degrades when τ is very low. The study also adopted an FCFS strategy for arbitrage selection, which issues arbitrages as soon as their optimal input is calculated, potentially saving valuable time. The revenue difference between the FCFS and profit-maximizing strategies was found to be more significant with increasing τ values, highlighting the need for further optimization of the FCFS strategy.

In conclusion, the study provides valuable insights into the presence of MEV on the Algorand blockchain and the efficiency of MEV searchers in exploiting arbitrage opportunities. The study also highlights the need for further optimization of arbitrage strategies and the potential for collecting transaction data on the mempool directly to detect opportunities during block construction. The findings of the study have important implications for the development of more efficient and competitive arbitrage strategies on the Algorand blockchain.