---
title: Superhuman Speed in Futures Trading Requires Stricter Regulation
url: https://www.ml-quant.com/papers/ssrn/4580566/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4580566
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4580566
featured: 2023-09-28
citations: unknown
topic: Trading, Microstructure & Execution
---


# Superhuman Speed in Futures Trading Requires Stricter Regulation

A study suggests that high-frequency traders' use of low-latency trading algorithms for arbitrage opportunities increases execution costs for other market participants, proposing batch auctions as a solution.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4580566
- Identifier: SSRN 4580566
- Released: 2023-09-22
- First featured: Quant Letter No. 17 (2023-09-28): https://www.ml-quant.com/issues/2023-09-28/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [High-Frequency Trading, Asset Pricing, and Market Microstructure](https://www.ml-quant.com/papers/ssrn/4858807/): A study using high-frequency trading data provides insights into asset pricing, transaction costs, investor liquidity asymmetry, and seasonality effects.
- [Optimal Execution under Incomplete Information](https://www.ml-quant.com/papers/arxiv/2411.04616/): The research introduces a high-frequency trading model using a hidden Markov process to examine optimal liquidation strategies under limited information, offering a practical algorithm to simulate the original liquidation issue.
- [HighFrequency Trading Impact](https://www.ml-quant.com/papers/repec/kap-fmktpm-v-33-y-2019-i-2-d-10-1007-s11408-019-00331-6/): The paper discusses the effects of high-frequency trading on market factors like volatility, transaction costs, and liquidity, indicating varied opinions in the financial sector.
- [Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning](https://www.ml-quant.com/papers/arxiv/2609.22785/): The research extends adversarial reinforcement learning for market making to handle self-exciting order arrivals and price impact, using an LSTM module to improve robustness in complex microstructure environments.
- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://www.ml-quant.com/papers/arxiv/2501.09747/): A new tokenization scheme, Frequency-space Action Sequence Tokenization (FAST), has been proposed for robot actions, facilitating the training of vision-language action policies for complex and high-frequency tasks.
- [Deep Reinforcement Learning for Active High Frequency Trading](https://www.ml-quant.com/papers/arxiv/2101.07107/): A new Deep Reinforcement Learning framework has been developed for high frequency stock trading, showing potential for profitable long-term strategies.
