Market-Making Simulations with Fill Probabilities and Adverse Selection

Summary

The paper examines how fill likelihood and adverse selection affect simulations of short-term market-making strategies. It studies a stochastic optimal-control strategy across liquid CME futures, including equity index, crude oil, and Treasury contracts. The central lesson is that simulation assumptions about whether limit orders fill, and what happens to prices around those fills, can materially change estimated performance.

The authors report empirical evidence that modeling fills more realistically and tracking adverse fills more carefully gives a more credible performance assessment. They caution that approaches which simulate price movements independently from market orders may overstate short-horizon strategy results. The excerpt does not provide the specific fill model, quantitative performance comparisons, or detailed experimental setup, so it supports the general simulation lesson but not a precise estimate of the bias.

Key ideas

• Fill probabilities can materially affect simulated market-making performance.
• Adverse fills should be tracked as part of strategy simulation.
• Simulating prices independently from market orders can inflate short-term strategy results.
• The study evaluates a stochastic optimal-control approach on several liquid futures contracts.

Tags

# Market Simulation under Adverse Selection


# Market Simulation under Adverse Selection









In this paper, we study the effects of fill probabilities and adverse fills on the trading strategy simulation process. We specifically focus on a stochastic optimal control market-making problem and test the strategy on ES (E-mini S\&P 500), NQ (E-mini Nasdaq 100), CL (Crude Oil) and ZN (10-Year Treasury Note), which are some of the most liquid futures contracts listed on the CME (Chicago Mercantile Exchange). We provide empirical evidence that shows how fill probabilities and adverse fills can significantly affect performance and propose a more prudent simulation framework to deal with this. Many previous works aim to measure different types of adverse selection in the limit order book (LOB), however, they often simulate price processes and market orders independently. This has the ability to largely inflate the performance of a short-term style trading strategy. Our studies show that using more realistic fill probabilities and tracking adverse fills in the strategy simulation process more accurately shows how these types of trading strategies would perform in reality.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.