With the recent rise of Machine Learning (ML) as a candidate to partially replace classic Financial Mathematics (FM) methodologies, we investigate the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two...

The change subsequent to the sub-prime crisis pushed pressure on decreased financial products complexity, going from exotics to vanilla options but increase in pricing efficiency. We introduce in this paper a more efficient methodology for vanilla option pricing using a scenario based particle...

We adopt deep learning models to directly optimize the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us to directly optimize portfolio weights by updating model parameters. Instead of selecting individual assets, we...

We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets. Initially observed within the confines of pairwise economic and fundamental ties, such as the stock-bond connection of the same company and stocks linked through supply-demand...