---
title: Liquidation Strategies' Effects in Multi-Asset Market
url: https://www.ml-quant.com/papers/repec/kap-compec-v-62-y-2023-i-4-d-10-1007-s10614-022-10316-9/
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: RePEc:kap:compec:v:62:y:2023:i:4:d:10.1007_s10614-022-10316-9
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10614-022-10316-9%3Bh%3Drepec%3Akap%3Acompec%3Av%3A62%3Ay%3A2023%3Ai%3A4%3Ad%3A10.1007_s10614-022-10316-9
featured: 2023-11-15
citations: unknown
topic: Trading, Microstructure & Execution
---


# Liquidation Strategies' Effects in Multi-Asset Market

The paper reveals that certain algorithmic trading strategies can lower liquidation costs but may negatively affect market indicators in a multi-asset artificial stock market.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10614-022-10316-9%3Bh%3Drepec%3Akap%3Acompec%3Av%3A62%3Ay%3A2023%3Ai%3A4%3Ad%3A10.1007_s10614-022-10316-9
- Identifier: RePEc:kap:compec:v:62:y:2023:i:4:d:10.1007_s10614-022-10316-9
- Released: 2023-11-15
- First featured: Quant Letter No. 26 (2023-11-15): https://www.ml-quant.com/issues/2023-11-15/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## 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.
- [Volume Weighted Average Price (VWAP) The Holy Grail for Day Trading Systems](https://www.ml-quant.com/papers/ssrn/4631351/): The article introduces a day trading strategy based on Volume Weighted Average Price (VWAP) that can identify market imbalances, resulting in a 671% return on a $25,000 investment.
- [Meme Stocks and Individual Investor Trading: Evidence from Reddit Outages](https://www.ml-quant.com/papers/ssrn/4629865/): The predictability of retail order imbalance on future returns for meme stocks increases during Reddit outages, indicating that intense discussions can disrupt individual investors' decisions.
- [Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models](https://www.ml-quant.com/papers/arxiv/2311.05743/): The research enhances a Deep Q-Network trading model using advanced methods, showing improved performance in automated trading and the potential of convolutional neural networks in trading systems.
- [The QLBS Model Within the Presence of Feedback Loops Through the Impacts of a Large Trader](https://www.ml-quant.com/papers/arxiv/2311.06790/): The QLBS model is expanded to include a large trader's impact on exchange rates and contingent claim prices, using reinforcement learning to find an optimal hedging strategy, reducing transaction costs and aligning with the trader's fair price.