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Detecting Toxic Flow

Author

Listed:
  • 'Alvaro Cartea
  • Gerardo Duran-Martin
  • Leandro S'anchez-Betancourt

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.

Suggested Citation

  • 'Alvaro Cartea & Gerardo Duran-Martin & Leandro S'anchez-Betancourt, 2023. "Detecting Toxic Flow," Papers 2312.05827, arXiv.org.
    • Handle: RePEc:arx:papers:2312.05827
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    References listed on IDEAS

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