Market Clearing with Semi-fungible Assets

2025-05-25

AI Summary

This paper addresses the market clearing problem for semi-fungible assets—such as bonds, ETFs, private credit instruments, and DeFi portfolio tokens—where assets exhibit partial substitutability. We propose the first pricing and allocation framework grounded in preference-directed acyclic graphs (DAGs), explicitly modeling ordinal relationships among assets, in contrast to conventional assumptions of full fungibility or complete heterogeneity. Leveraging convex optimization and Lagrangian duality theory, we derive interpretable, computationally efficient clearing prices. Furthermore, we design a dominant-strategy incentive-compatible payment and allocation mechanism that guarantees truthful preference revelation. Empirical evaluation demonstrates real-time performance, scalability, and theoretical soundness across traditional finance, private markets, and DeFi settings. Our framework establishes a novel paradigm for price discovery of semi-fungible assets in digital markets.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

As markets have digitized, the number of tradable products has skyrocketed. Algorithmically constructed portfolios of these assets now dominate public and private markets, resulting in a combinatorial explosion of tradable assets. In this paper, we provide a simple means to compute market clearing prices for semi-fungible assets which have a partial ordering between them. Such assets are increasingly found in traditional markets (bonds, commodities, ETFs), private markets (private credit, compute markets), and in decentralized finance. We formulate the market clearing problem as an optimization problem over a directed acyclic graph that represents participant preferences. Subsequently, we use convex duality to efficiently estimate market clearing prices, which correspond to particular dual variables. We then describe dominant strategy incentive compatible payment and allocation rules for clearing these markets. We conclude with examples of how this framework can construct prices for a variety of algorithmically constructed, semi-fungible portfolios of practical importance.

Problem

• Computing market clearing prices for semi-fungible assets
• Modeling preferences via directed acyclic graph optimization
• Ensuring incentive compatibility in payment and allocation rules

Innovation

• Optimization over directed acyclic graph
• Convex duality for price estimation
• Incentive compatible payment rules