---
title: Arbitrage Risk in MAX Effect
url: https://www.ml-quant.com/papers/ssrn/4775560/
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 4775560
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4775560
featured: 2024-04-03
citations: unknown
topic: Trading, Microstructure & Execution
---


# Arbitrage Risk in MAX Effect

In the Korean stock market, the MAX effect, or the highest daily return from the previous month, is only significant in overpriced stock groups.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4775560
- Identifier: SSRN 4775560
- Released: 2022-05-20
- First featured: Quant Letter No. 43 (2024-04-03): https://www.ml-quant.com/issues/2024-04-03/
- 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.
- [The Pricing of Liquidity Risk Factors](https://www.ml-quant.com/papers/ssrn/4779075/): The research investigates the pricing of liquidity factors in the US stock market, demonstrating that models with a liquidity factor outperform those with a size factor.
- [Improving Deep Learning of Alpha Term Structures from the Order Book](https://www.ml-quant.com/papers/ssrn/4770476/): The article evaluates the efficiency of four deep learning models in predicting high-frequency returns in equities, emphasizing the role of network structure, input choice, and time inclusion.
- [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.