Machine Learning Based Robust Optimization Models for Limit Order Books to Predict Price Movements

This paper develops a machine learning based robust optimization model to estimate structure of limit order books. The new architecture yields a low-dimensional model of price movements deep into the limit order book, allowing more effective use of information from deep in the limit order book (i.e., many levels beyond the best bid and best ask). Due to its more effective use of information deep in the limit order book, the spatial neural network especially outperforms the standard neural network in the tail of the distribution, which is important for risk management applications.Our data-driven approach offers new benefits for practical applications.