---
title: Asymmetric Stock Returns and Trading Volume Relationship
url: https://www.ml-quant.com/papers/repec/eme-rafpps-raf-02-2023-0045/
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:eme:rafpps:raf-02-2023-0045
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FRAF-02-2023-0045%2Ffull%2Fhtml%3Futm_source%3Drepec%26utm_medium%3Dfeed%26utm_campaign%3Drepec%3Bh%3Drepec%3Aeme%3Arafpps%3Araf-02-2023-0045
featured: 2024-04-24
citations: unknown
topic: Trading, Microstructure & Execution
---


# Asymmetric Stock Returns and Trading Volume Relationship

A study reveals differences in return-volume relationships for French SMEs and blue chips depending on market conditions and size.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FRAF-02-2023-0045%2Ffull%2Fhtml%3Futm_source%3Drepec%26utm_medium%3Dfeed%26utm_campaign%3Drepec%3Bh%3Drepec%3Aeme%3Arafpps%3Araf-02-2023-0045
- Identifier: RePEc:eme:rafpps:raf-02-2023-0045
- Released: 2023-07-04
- First featured: Quant Letter No. 46 (2024-04-24): https://www.ml-quant.com/issues/2024-04-24/
- 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.
- [Trading Volume Alpha](https://www.ml-quant.com/papers/ssrn/4802345/): The article emphasizes the importance of predicting trading volume in portfolio optimization, noting that the benefits can be as significant as those from return prediction.
- [Optimal Design of Automated Market Makers on Decentralized Exchanges](https://www.ml-quant.com/papers/arxiv/2404.13291/): The study presents a model for ideal liquidity provision in automated market makers, indicating that exchange rate volatility increases the optimal transaction fee and the pricing formula is tied to the performance of underlying assets.
- [Recommender Systems in Financial Trading: Using machine-based conviction analysis in an explainable AI investment framework](https://www.ml-quant.com/papers/arxiv/2404.11080/): Recommender Systems: The text explores the use of Artificial Intelligence, particularly Recommender Systems, to mimic traditional asset selection and portfolio construction, integrating AI data analytics with AI-based portfolio construction methods.
- [Is Liquidity Provision Informative? Evidence From Agricultural Futures Markets](https://www.ml-quant.com/papers/ssrn/4795329/): A study of the Chicago Mercantile Exchange's futures markets shows that aggressive trades and limit orders significantly contribute to price discovery, with most limit orders providing uninformed liquidity.
