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 aim of this technical document is threefold with the bigger picture being to contribute, within the challenging regulatory environment, to bring closer together traditional conflicting practices such as trading vs risk as well as risk responsiveness vs stability. In order to achieve this...

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...

In this paper we propose a new approach to studying electronic trading & systemic risk by re-introducing the High Frequency Trading Ecosystem (HFTE) model [71]. We specify an approach in which agents interact through a topological structure designed to address the complexity demands of most...

The financial industry is at the heart of our economy and fittingly comes under much scrutiny. Indeed, as a result of social and political pressure, particularly since the recent subprime crisis, more rigorous regulations have been imposed on both “authorized firms” and “approved...

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The...

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into...

In this work we show that prediction uncertainty estimates gleaned from deep learning models can be useful inputs for influencing the relative allocation of risk capital across trades. In this way, consideration of uncertainty is important because it permits the scaling of investment size across...

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...