---
title: Visual Attention and Herding in Asset Trading Game
url: https://www.ml-quant.com/papers/ssrn/4950045/
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 4950045
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4950045
featured: 2024-09-10
citations: unknown
topic: Trading, Microstructure & Execution
---


# Visual Attention and Herding in Asset Trading Game

A study using eye-tracking technology found that visual attention influences trading behavior, with attentional priority leading to a greater tendency to follow the crowd when information is disparate and tournament incentives are present.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4950045
- Identifier: SSRN 4950045
- Released: 2024-02-01
- 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: 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.
- [Attention-based reading, highlighting, and forecasting of the limit order book](https://www.ml-quant.com/papers/arxiv/2409.02277/): The study develops a superior forecasting method for multi-level limit order books using advanced multidimensional sequence-to-sequence models.
- [Deviations from the Nash equilibrium in a two-player optimal execution game with reinforcement learning](https://www.ml-quant.com/papers/arxiv/2408.11773/): Autonomous trading bots using advanced algorithms can disrupt markets by deviating from traditional predictions, often favoring optimal solutions over equilibrium.
- [A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets - A New Microfoundations of GARCH model](https://www.ml-quant.com/papers/arxiv/2409.12516/): The research explores how AI traders affect market price formation and volatility, providing a deeper understanding of the GARCH model and AI's impact on market dynamics.
- [Algorithmic and high-frequency trading problems for Semi-Markov and Hawkes jump-diffusion models](https://www.ml-quant.com/papers/arxiv/2409.12776/): The paper presents jump-diffusion models to understand limit order book data in algorithmic and high-frequency trading, offering optimal solutions for trading issues within a stochastic optimal control framework.