---
title: Do Limits to Arbitrage Explain Portfolio Gains from Asset Mispricing?
url: https://www.ml-quant.com/papers/ssrn/4760599/
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 4760599
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4760599
featured: 2024-03-20
citations: 0
topic: Trading, Microstructure & Execution
---


# Do Limits to Arbitrage Explain Portfolio Gains from Asset Mispricing?

The study suggests that portfolios can use asset mispricing to increase efficiency, particularly during high-sentiment periods.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4760599
- Identifier: SSRN 4760599
- Released: 2024-03-15
- First featured: Quant Letter No. 41 (2024-03-20): https://www.ml-quant.com/issues/2024-03-20/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [Aggregate Net Fund Redemptions and Liquidity of Stocks Holding](https://www.ml-quant.com/papers/ssrn/4609248/): Mutual fund investor withdrawals can adversely affect the liquidity of stock holdings, influenced by investor sentiment and stock returns.
- [DeepFake and Trading Signals](https://www.ml-quant.com/papers/ssrn/5070125/): Advanced AI-powered deepfake technology threatens financial markets by manipulating market sentiment, disrupting trading systems, and exploiting retail investors.
- [AI in Day Trading](https://www.ml-quant.com/papers/ssrn/5246516/): A new intraday trading strategy for the USTEC CFD, using a large language model to filter trading signals based on news sentiment, boosts profitability.
- [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.
- [Improving Merger Arbitrage Returns with Machine Learning](https://www.ml-quant.com/papers/ssrn/4765067/): Machine learning can enhance the profitability of merger arbitrage trades, offering valuable financial insights and substantial economic benefits for investors.