Market Simulation under Adverse Selection

TL;DR

This paper investigates how fill probabilities and adverse fills impact market-making strategy simulations, emphasizing the importance of realistic modeling to accurately assess performance in liquid futures markets.

Contribution

It introduces a more realistic simulation framework that accounts for adverse fills and fill probabilities, improving the accuracy of market-making strategy evaluations.

Findings

Fill probabilities and adverse fills significantly influence strategy performance.

Realistic simulation reduces overestimation of strategy effectiveness.

Empirical tests on major futures contracts validate the proposed approach.

Abstract

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.