quant research // summarizing the paperLOADING
Joseph Leclère, Youssef Ouazzani Chahdi, Mathieu Rosenbaum, Grégoire Szymanski · 2026-07-03
Market impact is how much your own trading moves the price versus what would have happened without you — but that "without you" path is never observable. The paper develops a mathematical method to exactly simulate counterfactual price paths using the same underlying market randomness, based on conditional simulation of point processes under changed intensities. This gives an event-driven algorithm to reconstruct the alternative path and measure impact for aggressive, passive, or mixed execution strategies.
Why it matters: Anyone concerned with execution costs — desks, algo traders, or large investors slicing orders — cares about how much their trading pushes prices. This framework offers a principled, apples-to-apples way to estimate that impact by comparing paths on the same realized randomness, which could sharpen transaction-cost analysis and execution strategy comparison.
⚠ This is a theoretical/methodological result relying on point-process modeling assumptions, not a demonstrated live-trading cost reduction, and requires modeling intensities correctly.
Market impact is defined as the difference between the observed price trajectory under a given execution strategy and the counterfactual trajectory that would have prevailed without it. Since this counterfactual is unobservable, estimating market impact requires simulating alternative paths under the same realized market randomness. We address this by studying the conditional simulation of point processes under perturbed intensities. Given an observed counting process whose intensity is determined by its own history, we characterize the conditional law of the latent Poisson random measure in a thinning representation. This yields an exact, event-driven algorithm that reconstructs counterfactual paths on a common randomness source, enabling rigorous pathwise market impact estimation for aggressive, passive, and mixed strategies.
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AI summary generated from the paper’s public abstract via arXiv; it may miss nuance — read the source before relying on it. Thank you to arXiv for its open-access interoperability; StockTools is not affiliated with arXiv, and all rights remain with the authors. Educational only, not financial advice.