Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

We employ deep learning in forecasting high‐frequency returns at multiple horizons for 115
stocks traded on Nasdaq using order book information at the most granular level. While raw …

We use statistically validated networks, a recently introduced method of validating links in a
bipartite system, to identify clusters of investors trading in a financial market. Specifically, we …

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or
sell) are positively autocorrelated out to time lags of tens of thousands of orders …

Computer trading in financial markets is a natural and inevitable consequence of
technological progress, and is almost as old as computers themselves. Computers facilitate …

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 examine a data-set of institutional trades where approximately one-fourth of the trades
were labelled as having been created for cash flow purposes. We aggregate near …

We consider a model of linear market impact, and address the problem of replicating a
contingent claim in this framework. We derive a nonlinear Black–Scholes equation that …

High-frequency market making is a liquidity-providing trading strategy that simultaneously
generates many bids and asks for a security at ultra-low latency while maintaining a …

Market impact is a key concept in the study of financial markets and several models have
been proposed in the literature so far. The propagator model posits that the price at high …