---
title: Insider Trading Model
url: https://www.ml-quant.com/papers/repec/eee-apmaco-v-488-y-2025-i-c-s0096300324005812/
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:eee:apmaco:v:488:y:2025:i:c:s0096300324005812
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0096300324005812%3Bh%3Drepec%3Aeee%3Aapmaco%3Av%3A488%3Ay%3A2025%3Ai%3Ac%3As0096300324005812
featured: 2025-01-01
citations: unknown
topic: Trading, Microstructure & Execution
---


# Insider Trading Model

The article introduces a new continuous-time insider trading model, showing that a higher correlation coefficient makes the equilibrium price more informative, reducing trading intensity and the insider's expected payoff.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0096300324005812%3Bh%3Drepec%3Aeee%3Aapmaco%3Av%3A488%3Ay%3A2025%3Ai%3Ac%3As0096300324005812
- Identifier: RePEc:eee:apmaco:v:488:y:2025:i:c:s0096300324005812
- Released: 2025-01-01
- First featured: Quant Letter No. 80 (2025-01-01): https://www.ml-quant.com/issues/2025-01-01/
- 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.
- [Strategic Learning and Trading in Broker-Mediated Markets](https://www.ml-quant.com/papers/arxiv/2412.20847/): The research shows brokers in a mediated market have a strategic edge over traders who only use prices for information, and that brokers can gain economic value from leaked information.
- [Market Making With Fads, Informed, and Uninformed Traders](https://www.ml-quant.com/papers/arxiv/2501.03658/): A study explores solutions to an optimal liquidity provision problem in a market with different types of traders, aiming to extract market trend information from anonymous orders.
- [High-frequency lead-lag relationships in the Chinese stock index futures market: tick-by-tick dynamics of calendar spreads](https://www.ml-quant.com/papers/arxiv/2501.03171/): The research examines the relationships between different maturity stock index futures contracts in China's financial futures market, uncovering a regular pattern of price discovery and a profitable trading strategy based on this pattern.
- [An Application of the Ornstein-Uhlenbeck Process to Pairs Trading](https://www.ml-quant.com/papers/arxiv/2412.12458/): A preliminary study shows that a pairs trading strategy using the Ornstein-Uhlenbeck process captures trends but underperforms due to non-stationary pairs and parameter tuning limitations.