---
title: When Collateral Prices Count: Recognition Clocks and Liquidation Batches
url: https://www.ml-quant.com/papers/ssrn/7665698/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-30
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7665698
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665698
featured: 2026-10-30
citations: unknown
topic: Trading, Microstructure & Execution
---


# When Collateral Prices Count: Recognition Clocks and Liquidation Batches

Research on decentralized lending shows that collateral revaluation rules predictably determine when and how many positions are liquidated together, with large price moves triggering batch liquidations far exceeding single-position boundaries.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665698
- Identifier: SSRN 7665698
- Released: 2026-10-24
- First featured: Quant Letter No. 137 (2026-10-30): https://www.ml-quant.com/issues/2026-10-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution
- Authors: Fernando Hernandez-Atienza

## Related

- [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.
- [SwingGuard: Risk-Conditioned Multi-Agent Swing Trading with Dual-Timescale Governance](https://www.ml-quant.com/papers/arxiv/2610.12988/): A dual-timescale multi-agent framework generates trading strategies conditioned on current risk and maintains ongoing position governance between strategy revisions on cryptocurrency, preserving returns after transaction costs.
- [TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure](https://www.ml-quant.com/papers/arxiv/2602.23784/): A Model for Trade-Flow in Market Microstructure: TradeFM is a new AI model that improves the analysis of market structures by studying billions of trade events in stocks, leading to better simulations of financial returns.
- [Romania's Roadmap to a Greener Financial System: An analysis of Environmental, Social and Governance Reporting on the Bucharest Exchange Trading Index](https://www.ml-quant.com/papers/ssrn/4440516/): Romania struggles to attract sustainable investments because its major companies have low transparency and high greenhouse gas emissions.
- [Robust insurance pricing and liquidity management](https://www.ml-quant.com/papers/arxiv/2510.15709/): Accounting for model uncertainty makes insurers set higher, more conservative prices and liquidity buffers, widens capacity ranges, and produces much longer underwriting cycles with more time in low‑capacity states.
