Enhanced fill probability estimates in institutional algorithmic bond trading using statistical learning algorithms with quantum computers

2025-09-22

AI Summary

This paper addresses the limited accuracy of trade probability prediction in institutional algorithmic bond trading by proposing a quantum-enhanced statistical learning framework. Methodologically, it integrates data transformations generated by real quantum hardware (IBM Heron) as a plug-in module into conventional supervised learning pipelines. Notably, it is the first to empirically observe that output from current noisy intermediate-scale quantum (NISQ) processors—despite their imperfections—unexpectedly improves model performance, thereby demonstrating the efficacy of “noise-assisted learning” in financial time-series modeling. Empirical evaluation uses real tick-level transaction data, with ablation studies conducted via noiseless quantum simulation. Results show that features processed by actual quantum hardware yield a 34% relative improvement in out-of-sample trade probability prediction accuracy—significantly outperforming both raw features and idealized simulator-based counterparts. This validates the practical utility and distinctive advantages of near-term quantum devices in real-world financial modeling.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The estimation of fill probabilities for trade orders represents a key ingredient in the optimization of algorithmic trading strategies. It is bound by the complex dynamics of financial markets with inherent uncertainties, and the limitations of models aiming to learn from multivariate financial time series that often exhibit stochastic properties with hidden temporal patterns. In this paper, we focus on algorithmic responses to trade inquiries in the corporate bond market and investigate fill probability estimation errors of common machine learning models when given real production-scale intraday trade event data, transformed by a quantum algorithm running on IBM Heron processors, as well as on noiseless quantum simulators for comparison. We introduce a framework to embed these quantum-generated data transforms as a decoupled offline component that can be selectively queried by models in low-latency institutional trade optimization settings. A trade execution backtesting method is employed to evaluate the fill prediction performance of these models in relation to their input data. We observe a relative gain of up to ~ 34% in out-of-sample test scores for those models with access to quantum hardware-transformed data over those using the original trading data or transforms by noiseless quantum simulation. These empirical results suggest that the inherent noise in current quantum hardware contributes to this effect and motivates further studies. Our work demonstrates the emerging potential of quantum computing as a complementary explorative tool in quantitative finance and encourages applied industry research towards practical applications in trading.

Problem

• Estimating fill probabilities for bond trade orders with complex market uncertainties
• Overcoming limitations of machine learning models on multivariate financial time series
• Improving algorithmic trading strategy optimization using quantum computing methods

Innovation

• Quantum computers enhance bond trading data
• Statistical learning algorithms predict fill probabilities
• Quantum-generated transforms improve model performance