Detecting toxic flow

Álvaro Cartea, Gerardo Duran-Martin and Leandro Sánchez-Betancourt

Quantitative Finance, 2026, vol. 26, issue 4, 541-561

Abstract: This paper develops a framework to predict toxic trades that a broker receives from her clients. Toxic trades are predicted with a novel online learning Bayesian method which we call the projection-based unification of last-layer and subspace estimation (PULSE). PULSE is a fast and statistically-efficient Bayesian procedure for online training of neural networks. We employ a proprietary dataset of foreign exchange transactions to test our methodology. Neural networks trained with PULSE outperform standard machine learning and statistical methods when predicting if a trade will be toxic; the benchmark methods are logistic regression, random forests, and a recursively-updated maximum-likelihood estimator. We devise a strategy for the broker who uses toxicity predictions to internalise or to externalise each trade received from her clients. Our methodology can be implemented in real-time because it takes less than one millisecond to update parameters and make a prediction. Compared with the benchmarks, online learning of a neural network with PULSE attains the highest PnL and avoids the most losses by externalising toxic trades.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:26:y:2026:i:4:p:541-561

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DOI: 10.1080/14697688.2026.2619539

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