---
title: NPS Internal vs. External Management: Market Impact of Trading Strategies
url: https://www.ml-quant.com/papers/repec/eme-jdqspp-jdqs-12-2022-0027/
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:eme:jdqspp:jdqs-12-2022-0027
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FJDQS-12-2022-0027%2Ffull%2Fhtml%3Futm_source%3Drepec%26utm_medium%3Dfeed%26utm_campaign%3Drepec%3Bh%3Drepec%3Aeme%3Ajdqspp%3Ajdqs-12-2022-0027
featured: 2023-08-30
citations: unknown
topic: Trading, Microstructure & Execution
---


# NPS Internal vs. External Management: Market Impact of Trading Strategies

Market Impact of Trading Strategies: The research investigates the trading strategies and market impact of the National Pension Service of Korea, identifying differences between internal and external management methods, with the latter serving as a market stabilizer and the former increasing volatility.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FJDQS-12-2022-0027%2Ffull%2Fhtml%3Futm_source%3Drepec%26utm_medium%3Dfeed%26utm_campaign%3Drepec%3Bh%3Drepec%3Aeme%3Ajdqspp%3Ajdqs-12-2022-0027
- Identifier: RePEc:eme:jdqspp:jdqs-12-2022-0027
- Released: 2023-08-30
- First featured: Quant Letter No. 14 (2023-08-30): https://www.ml-quant.com/issues/2023-08-30/
- 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.
- [Identifying High Frequency Trading Activity without Proprietary Data](https://www.ml-quant.com/papers/ssrn/4551238/): The article evaluates the reliability of commonly used indicators to identify high frequency traders, showing variations in performance and suggesting that unscaled proxies are more effective at indicating true HFT activity.
- [JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading](https://www.ml-quant.com/papers/arxiv/2308.13289/): JAX-LOB: The paper introduces JAX-LOB, the first GPU-powered limit order book simulator capable of processing multiple books simultaneously, designed for efficient large-scale simulations of LOB dynamics for research, calibration, and reinforcement learning training.
- [Solvability of Differential Riccati Equations and Applications to Algorithmic Trading with Signals](https://www.ml-quant.com/papers/arxiv/2202.07478/): The research investigates a differential Riccati equation with indefinite matrix coefficients, using it to address two algorithmic trading issues with a constant absolute risk-aversion utility function.
- [Optimal Trading with Signals and Stochastic Price Impact](https://www.ml-quant.com/papers/arxiv/2101.10053/): The research uses singular perturbation methods to study optimal trading in a market with stochastic price impact, showing how stochastic trading frictions affect optimal trading through numerical experiments.