Agent-based modeling (ABM) is a novel computational methodology for representing the
behavior of individuals in order to study social phenomena. Its use is rapidly growing in …

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 make an extensive empirical study of the market impact of large orders (metaorders)
executed in the US equity market between 2007 and 2009. We show that the square root …

Crashes have fascinated and baffled many canny observers of financial markets. In the strict
orthodoxy of the efficient market theory, crashes must be due to sudden changes of the …

Prediction of financial market data with deep learning models has achieved some level of
recent success. However, historical financial data suffer from an unknowable state space …

It is difficult to argue against the fact that research has focussed on artificial intelligence (AI)
and robotisation over the past few decades. Additionally, during the past several years, it …

Abstract The Lillo–Mike–Farmer (LMF) model is an established econophysics model
describing the order-splitting behaviour of institutional investors in financial markets. In the …

We study price formation in intraday electricity markets in the presence of asymmetric
information and intermittent generation. We use stochastic control theory to identify optimal …

We present a thorough empirical analysis of market impact on the Bitcoin/USD exchange
market using a complete dataset that allows us to reconstruct more than one million …

Financial markets display a host of universal “stylized facts” begging for a scientific
explanation: Excess volatility, fat tails, and clustered activity are well known and have been …