Search Results for author:
Found 3 papers, 2 papers with code
The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective
The performance of the deep learning models is strongly dependent on the choice of order book representation, and in this respect, the volume representation appears to have multiple practical advantages.
Non-average price impact in order-driven markets
We present a measurement of price impact in order-driven markets that does not require averages across executions or scenarios.
$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
