Detecting Toxic Flow

• URL: http://arxiv.org/abs/2312.05827v1
• Date: Sun, 10 Dec 2023 09:00:09 GMT
• Title: Detecting Toxic Flow
• Authors: \'Alvaro Cartea, Gerardo Duran-Martin, Leandro S\'anchez-Betancourt
• Abstract summary: This paper develops a framework to predict toxic trades that a broker receives from her clients. We use a proprietary dataset of foreign exchange transactions to test our methodology. We devise a strategy for the broker who uses toxicity predictions to internalise or to externalise each trade received from her clients.
• Score: 0.40964539027092917
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 Bayesian method which we call the projection-based unification of last-layer and subspace estimation (PULSE). PULSE is a fast and statistically-efficient online procedure to train a Bayesian neural network sequentially. We employ a proprietary dataset of foreign exchange transactions to test our methodology. PULSE outperforms 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, PULSE attains the highest PnL and the largest avoided loss for the horizons we consider.

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