Paper: arXiv 2503.04072

Abstract

In this paper, we introduce a robust market making framework based on Wasserstein distance, utilizing a stochastic policy approach enhanced by entropy regularization. We demonstrate that, under mild assumptions, the robust market making problem can be reformulated as a convex optimization question. Additionally, we outline a methodology for selecting the optimal radius of the Wasserstein ball, further refining our framework’s effectiveness.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents a complex theoretical framework involving Wasserstein distance, convex optimization, and entropy regularization, with heavy reliance on mathematical derivations and assumptions. However, it lacks empirical validation through backtests, code implementations, or statistical metrics, remaining primarily in the theoretical domain.

Research Flowchart

  flowchart TD
  A["Research Goal: Robust Market Making"] --> B["Model Formulation<br/>Wasserstein Distance &<br/>Entropy-Regularized Stochastic Policy"]
  B --> C["Reformulation Step<br/>Proof: Convex Optimization Problem"]
  C --> D["Data: Equity Market Simulations<br/>& Historical Microstructure"]
  D --> E["Computational Process<br/>Optimal Spread Calculation<br/>& Wasserstein Ball Radius Selection"]
  E --> F["Key Outcome 1: Robust Pricing Strategy<br/>Validated on Simulated/Real Data"]
  E --> G["Key Outcome 2: Scalable Algorithm<br/>via Entropy Regularization"]