Microstructure Dynamics and ML in Trading
Microstructure Dynamics and ML in Trading


One of the main components of optimally scheduling portfolio trading is the estimation of intraday and multi-day propagation of risk and market impact. This estimation is dependent on accurately modelling the dynamics involved across multiple scales: ranging from the daily to the tick scale.
This talk is concerned with how machine learning can be used to inform those dynamical models, exploring a range of techniques, including LSTMs, CNNs, Bayesian inference and Clustering methods.
One of our examples is concerned with the real-time prediction of liquidity, as it is, in general, one of the most important inputs to trading models. Observing that liquidity shocks, which can be both positive and negative, can be significant, we create a framework that can update the models’ predictions for those shocks for different horizons. Utilising that framework, we test Classical, Bayesian as well as Neural Network techniques, where we find that Neural Network architectures can indeed provide an edge in real-time liquidity prediction.
Beyond predicting market variables such as liquidity, we also demonstrate that, by using unsupervised learning techniques, such as clustering, on market microstructure features, we can better identify homogeneous groups among stocks. There are many applications of these stock clusters, including being a quick proxy of trading difficulty as well as an anomaly detection tool. Most importantly, however, they allow us to divide the global trading universe before calibrating cluster-specific trading models, while moving away from typical country and sector groupings. This not only helps in improving model calibration, but it also provides new insights regarding intraday trading phenomena.


Michael Steliaros is the global head of Quantitative Execution Services at Goldman Sachs. He is responsible for the research, development, and implementation of quantitative processes for portfolio and electronic trading as well as managing the bank's relations with the quantitative client base. Previously, Michael held a variety of senior roles at BofAML in London and New York, most recently running the global agency portfolio trading and quantitative equity businesses. Earlier in his career, he spent a decade on the buy-side (most notably BGI and Winton) building quant stock-selection models and managing global market-neutral equity portfolios. Michael received a bachelor's degree in Economics & Econometrics from the University of Nottingham, and an MSc and PhD in Finance from City University (CASS) Business School in London.

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