Causal Interventions in Bond Multi-Dealer-to-Client Platforms

TL;DR

This paper develops a probabilistic graphical model framework to analyze and optimize the request-for-quote process in bond trading platforms, integrating causal inference with machine learning for improved dealer strategies.

Contribution

It introduces a novel framework combining causal inference and probabilistic models to analyze RfQ processes in MD2C platforms, enhancing decision-making and profitability analysis.

Findings

Generative models match LightGBM's predictive accuracy (ROC-AUC: 0.742 vs. 0.743).

Models enforce spread monotonicity, aligning with business needs.

Framework provides insights into optimal pricing and client interest estimation.

Abstract

The digitalization of financial markets has shifted trading from voice to electronic channels, with Multi-Dealer-to-Client (MD2C) platforms now enabling clients to request quotes (RfQs) for financial instruments like bonds from multiple dealers simultaneously. In this competitive landscape, dealers cannot see each other's prices, making a rigorous analysis of the negotiation process crucial to ensure their profitability. This article introduces a novel general framework for analyzing the RfQ process using probabilistic graphical models and causal inference. Within this framework, we explore different inferential questions that are relevant for dealers participating in MD2C platforms, such as the computation of optimal prices, estimating potential revenues and the identification of clients that might be interested in trading the dealer's axes. We then move into analyzing two different approaches for model specification: a generative model built on the work of (Fermanian, Gu\'eant, \& Pu, 2017); and discriminative models utilizing machine learning techniques. Our results show that generative models can match the predictive accuracy of leading discriminative algorithms such as LightGBM (ROC-AUC: 0.742 vs. 0.743) while simultaneously enforcing critical business requirements, notably spread monotonicity.