Market Simulation under Adverse Selection

Paper: arXiv 2409.12721

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.

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 6.0/10 (capped from 7.0: no out-of-sample test; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced stochastic optimal control (SOC) and probability measures for market-making, indicating high mathematical complexity, while testing on multiple real futures contracts with empirical evidence on fill probabilities and adverse fills demonstrates strong empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Quantify impact of fill probabilities & adverse fills on market-making strategy performance"] --> B["Data: Historical Limit Order Book Data"]
  B --> C["Methodology: Stochastic Optimal Control Market-Making Model"]
  C --> D{"Simulation Approaches"}
  D --> E["Naive Simulation<br>Independent Price/Order Matching"]
  D --> F["Prudent Simulation<br>Integrating Realistic Fill Probabilities & Adverse Fill Tracking"]
  E & F --> G["Computational Process: Backtesting on CME Futures ES, NQ, CL, ZN"]
  G --> H["Key Findings: Naive simulation inflates performance; Prudent simulation yields realistic performance metrics"]

Cited References (25)

1. Deep attentive survival analysis in limit order books: Estimating fill probabilities with convolutional-transformers (2024)
2. An empirical analysis of the limit order book and the order flow in the Paris Bourse (1995)
3. Price discovery without trading: Evidence from limit orders (2019)
4. Optimal execution of limit and market orders with trade director, speed limiter, and fill uncertainty (2017)
5. Enhancing trading strategies with order book signals (2018)
6. Algorithmic and high-frequency trading (2015)
7. Buy low, sell high: A high frequency trading perspective (2014)
8. Algorithmic trading, stochastic control, and mutually exciting processes (2018)

3 of 25 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.