---
title: Intraday Dynamics of NASDAQ Stocks in the Electronic Trading Era: Uncovering Strong U-Shape Patterns in Trading Volume and Bid-Ask Spread
url: https://www.ml-quant.com/papers/ssrn/4792199/
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 4792199
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4792199
featured: 2024-04-17
citations: 0
topic: Trading, Microstructure & Execution
---


# Intraday Dynamics of NASDAQ Stocks in the Electronic Trading Era: Uncovering Strong U-Shape Patterns in Trading Volume and Bid-Ask Spread

NASDAQ stocks exhibit a U-shaped pattern in bid-ask spreads and trading volumes due to aggressive trading at market open and close, especially for smaller stocks and those with larger order imbalances.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4792199
- Identifier: SSRN 4792199
- Released: 2024-04-12
- First featured: Quant Letter No. 45 (2024-04-17): https://www.ml-quant.com/issues/2024-04-17/
- Citations (Semantic Scholar): 0
- 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.
- [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.
- [Strategic Informed Trading and the Value of Private Information](https://www.ml-quant.com/papers/arxiv/2404.08757/): The paper investigates market-clearing equilibrium in a risky financial market, showing that insider welfare increases with signal precision and price impact can both benefit and harm traders.
- [Social media emotions and market behavior](https://www.ml-quant.com/papers/arxiv/2404.03792/): The research finds that investor emotions expressed on social media can predict daily asset price movements, particularly in low liquidity or high short interest situations.
- [StockGPT: A GenAI Model for Stock Prediction and Trading](https://www.ml-quant.com/papers/arxiv/2404.05101/): The study introduces StockGPT, a model that predicts stock return dynamics using AI, showcasing the potential of AI in complex financial investment decisions.
