We develop a large-scale deep learning model to predict price movements from limit order
book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the …

The design of trading algorithms requires sophisticated mathematical models backed up by
reliable data. In this textbook, the authors develop models for algorithmic trading in contexts …

This paper develops a new neural network architecture for modeling spatial distributions (ie
distributions on R d) which is more computationally efficient than a traditional fully …

We use high-frequency data from the Nasdaq exchange to build a measure of volume
imbalance in the limit order (LO) book. We show that our measure is a good predictor of the …

We incorporate a Markovian signal in the optimal trading framework which was initially
proposed by Gatheral et al.(Math. Finance 22: 445–474, 2012) and provide results on the …

The effective bid-ask spread measured relative to the spread midpoint overstates the true
effective bid-ask spread in markets with discrete prices and elastic liquidity demand. The …

We study optimal liquidation in the presence of linear temporary and transient price impact
along with taking into account a general price predicting finite-variation signal. We formulate …

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This paper is split in three parts: first, we use labeled trade data to exhibit how market
participants' decisions depend on liquidity imbalance; then, we develop a stochastic control …

We propose an analytically tractable class of models for the dynamics of a limit order book,
described through a stochastic partial differential equation with multiplicative noise for the …