Auto-Regressive Control of Execution Costs

TL;DR

This paper introduces an iterative method for investors to dynamically estimate market parameters and optimize execution costs over time, addressing the challenge of limited initial market knowledge.

Contribution

It proposes a novel approach that enables uninformed investors to adaptively learn and optimize trade execution strategies through iterative OLS estimates.

Findings

Demonstrates effective parameter estimation from market data.

Shows improved execution cost management over time.

Provides a practical framework for uninformed market participants.

Abstract

Bertsimas and Lo's seminal work established a foundational framework for addressing the implementation shortfall dilemma faced by large institutional investors. Their models emphasized the critical role of accurate knowledge of market microstructure and price/information dynamics in optimizing trades to minimize execution costs. However, this paper recognizes that perfect initial knowledge may not be a realistic assumption for new investors entering the market. Therefore, this study aims to bridge this gap by proposing an approach that iteratively derives OLS estimates of the market parameters from period to period. This methodology enables uninformed investors to engage in the market dynamically, adjusting their strategies over time based on evolving estimates, thus offering a practical solution for navigating the complexities of execution cost optimization without perfect initial knowledge.