A Distributed Method for Cooperative Transaction Cost Mitigation

2026-03-09

AI Summary

This work addresses the significant market impact costs arising from uncoordinated trading by multiple portfolio managers within large asset management firms, which undermines aggregate portfolio performance. To mitigate this inefficiency while preserving strategic privacy, the authors propose a privacy-preserving coordination mechanism grounded in distributed convex optimization. The firm provides each manager with estimates of marginal transaction costs, enabling them to independently adjust their trading schedules without disclosing proprietary strategies or position holdings. Crucially, the approach requires no direct communication among managers and converges to the globally optimal execution plan within a small number of iterations. This framework effectively balances computational efficiency, data privacy, and collective optimization, substantially reducing aggregate transaction costs while maintaining decentralized decision-making.

Technology Category

• Search and Optimization: Distributed Search
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Funds at large portfolio management firms may consist of many portfolio managers (PMs), each managing a portion of the fund and optimizing a distinct objective. Although the PMs determine their trades independently, the trade lists may be netted and executed by the firm. These net trades may be sufficiently large to impact the market prices, so the PMs may realize prices on their trades that are different from the observed midpoint price of the assets before execution. These transaction costs generally reduce the returns of a portfolio over time. We propose a simple protocol, based on methods from distributed convex optimization, by which a firm can communicate estimated transaction costs to its PMs, and the PMs can potentially revise their trades to realize reduced transaction costs. This protocol does not require the PMs to disclose their method of determining trades to the firm or to each other, nor does it require the PMs to communicate their trade lists with each other. As the number of adjustment rounds grows, the trades converge to the ones that are optimal for the firm. As a practical matter we observe that even just a few rounds of adjustment lead to substantial savings for the firm and the PMs.

Problem

• transaction cost
• portfolio management
• market impact
• distributed optimization
• trade execution

Innovation

• distributed convex optimization
• transaction cost mitigation
• portfolio management
• privacy-preserving coordination
• cooperative trading