# Portfolio liquidation under transient price impact -- theoretical solution and implementation with 100 NASDAQ stocks

**Authors:** Ying Chen, Ulrich Horst, Hoang Hai Tran  
**arXiv:** [1912.06426](https://arxiv.org/abs/1912.06426)  
**Date:** 2019-12-13  
**Summary page:** https://www.summarizepaper.com/en/paper/1912.06426v1/

## Key points

- Authors Ying Chen, Ulrich Horst, and Hoang Hai Tran present an explicit solution for deterministic market impact parameters within the Graewe and Horst (2017) portfolio liquidation model.
- The model integrates various forms of market impact: instantaneous, permanent, and temporary effects.
- The solutions encompass benchmark models proposed by Almgren and Chris (2001) as well as Obizhaeva and Wang (2013), demonstrating versatility and applicability.
- The authors analyze the relationship between different types of market impact and the microstructure of limit order book markets.
- They estimate impact parameters effectively through analyzing public market data.
- Their optimal trading strategy using high-frequency limit order books from 100 NASDAQ stocks shows substantial cost savings compared to existing benchmark models.
- The research contributes valuable insights into portfolio liquidation strategies under transient price impact conditions, offering a theoretical framework and practical implementation considerations for optimizing trading performance in dynamic financial markets.

## Summary

In their paper titled "Portfolio Liquidation under Transient Price Impact - Theoretical Solution and Implementation with 100 NASDAQ Stocks," authors Ying Chen, Ulrich Horst, and Hoang Hai Tran present an explicit solution for deterministic market impact parameters within the framework of the Graewe and Horst (2017) portfolio liquidation model. This model integrates various forms of market impact, including instantaneous, permanent, and temporary effects. The authors demonstrate that their solutions encompass benchmark models proposed by Almgren and Chris (2001) as well as Obizhaeva and Wang (2013), showcasing the versatility and applicability of their approach. Furthermore, they delve into the relationship between different types of market impact and the underlying microstructure of limit order book markets. By analyzing public market data, they effectively estimate these impact parameters. Through a detailed investigation into the numerical performance of their optimal trading strategy using high-frequency limit order books from 100 NASDAQ stocks representing diverse market impact profiles, the authors showcase substantial cost savings compared to existing benchmark models. Overall, this research contributes valuable insights into portfolio liquidation strategies under transient price impact conditions. It offers a comprehensive theoretical framework alongside practical implementation considerations for optimizing trading performance in dynamic financial markets.