The promising predictive power of machine learning (ML) models is encouraging a wide range of applications, but the weak interpretability of their black-box feature is a major obstacle to their application to finance. The large scale of limit order book (LOB) data provides fertile ground for ML...

In theory, banning short selling stabilizes stock prices but undermines pricing efficiency and has ambiguous impacts on market liquidity. Empirical studies find mixed and conflicting results. This paper leverages cross-country policy variation during the 2020 Covid crisis to assess differential...

Theoretical predictions about market liquidity are usually hard to test empirically because they are expressed in terms of hard-to-observe quantities. We bridge the gap between theory and practice by adding ameta-model comprised of several generic equations shared by most models. The approach...

We develop spectral volume models to systematically estimate, explain, and exploit the high-frequency periodicity in intraday trading activities using Fourier analysis. The framework consistently recovers periodicities at specific frequencies in three steps, despite their low signal-to-noise...

We develop a theoretical framework and propose a relevant empirical analysis of the soybean complex prices cointegration relationship in a high-frequency setting. We allow for heterogeneous expectations among traders on the multi-asset price dynamics and characterize the resulting market...