Paper: arXiv 2405.18936

Abstract

Minimizing execution costs for large orders is a fundamental challenge in finance. Firms often depend on brokers to manage their trades due to limited internal resources for optimizing trading strategies. This paper presents a methodology for evaluating the effectiveness of broker execution algorithms using trading data. We focus on two primary cost components: a linear cost that quantifies short-term execution quality and a quadratic cost associated with the price impact of trades. Using a model with transient price impact, we derive analytical formulas for estimating these costs. Furthermore, we enhance estimation accuracy by introducing novel methods such as weighting price changes based on their expected impact content. Our results demonstrate substantial improvements in estimating both linear and impact costs, providing a robust and efficient framework for selecting the most cost-effective brokers.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced stochastic calculus and deriving analytical estimators for cost components, demonstrating high mathematical density. However, it lacks concrete backtesting results, specific dataset details, or implementation code, relying primarily on theoretical model analysis and generalized numerical simulations.

Research Flowchart

  flowchart TD
  A["Research Goal: Evaluate broker performance by minimizing execution costs for large orders"] --> B["Methodology: Modeling execution costs using transient price impact"]
  B --> C["Data: Historical trading data from equities"]
  C --> D["Computational Processes: Derive analytical formulas for linear & quadratic impact costs; apply weighted price change estimation"]
  D --> E["Outcomes: Improved estimation accuracy for both cost components"]
  E --> F["Outcome: Robust framework for selecting cost-effective brokers"]