---
title: Mitigating AI Crashes
url: https://www.ml-quant.com/papers/ssrn/4950688/
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 4950688
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4950688
featured: 2024-09-10
citations: unknown
topic: ML & AI Methods
---


# Mitigating AI Crashes

A study explores the pros and cons of merging high-frequency trading with artificial intelligence, proposing regulatory steps to ensure market stability.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4950688
- Identifier: SSRN 4950688
- Released: 2024-06-06
- First featured: Quant Letter No. 65 (2024-09-10): https://www.ml-quant.com/issues/2024-09-10/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network](https://www.ml-quant.com/papers/arxiv/2303.16532/): A new model for predicting futures prices in high-frequency trading, using graph neural networks, outperforms existing models in China's futures market.
- [Hawkes Model Parameter Estimation with Recurrent Neural Networks](https://www.ml-quant.com/papers/repec/eee-finlet-v-55-y-2023-i-pa-s1544612323002945/): A recurrent neural network was used to estimate parameters of a Hawkes model using high-frequency financial data, showing faster performance and similar accuracy to traditional methods, allowing for real-time volatility measurement.
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://www.ml-quant.com/papers/arxiv/2405.21060/): The research identifies a link between state-space models and Transformers in deep learning, leading to the creation of a faster language modeling architecture, Mamba-2.
- [Octo: An Open-Source Generalist Robot Policy](https://www.ml-quant.com/papers/arxiv/2405.12213/): Octo is a large transformer-based policy for robotic manipulation, trained on a vast dataset, that can be instructed via language or images and adapted to new domains.
- [Mastering Diverse Domains through World Models](https://www.ml-quant.com/papers/arxiv/2301.04104/): Algorithm Mastery: DreamerV3, a universal algorithm, excels in over 150 varied tasks, including diamond collection in Minecraft without human input, expanding the scope of reinforcement learning.
