Core Insights - David Itkin, an assistant professor at LSE, has developed a new method for assessing price impact in trade portfolios, which has been recognized with the Risk.net award for rising star in quant finance [1] - The research simplifies previous methods and demonstrates that a linear approach can effectively model nonlinear price impacts, providing a practical solution for portfolio managers [10][18] Research Development - The idea originated from Peter Schmidt, who was exploring portfolio optimization strategies during his master's at Imperial in 2022, focusing on the relationship between price impact and other factors [2] - Johannes Muhle-Karbe proposed extending the research into a full paper, leading to collaboration with Itkin, who contributed to the theoretical framework [3] Methodology - The paper titled "Tackling nonlinear price impact with linear strategies" was submitted in 2023 and published in the following year, addressing the common assumption of quadratic costs in portfolio optimization [3] - Itkin's approach involves selecting the right linear strategy through an optimization procedure, which simplifies the modeling of price impact [5][8] Findings - The research reveals that while naive linear strategies can lead to performance losses, optimizing the "effective" quadratic cost parameter can enhance performance [7] - The proposed method allows for analytical calculations without the need for computationally intensive simulations, making it accessible for practitioners [10] Performance Comparison - The linear policy developed in the research was benchmarked against a nonlinear optimizer, showing a 2% performance drop, which may be acceptable for many firms given the lower computational costs [17] - The findings provide reassurance to portfolio managers that approximations can yield nearly optimal results, addressing a significant concern in the industry [18] Future Directions - A follow-up paper is in progress, which will incorporate the effects of impact decay and contribute further to the understanding of price impact functions using neural network strategies [18]