Abstract Machine learning models are increasingly used in a wide variety of financial settings.
The difficulty of understanding the inner workings of these systems, combined with their …

This paper investigates various machine learning trading and portfolio optimisation models
and techniques. The notebooks to this paper are Python based. By last count there are about …

This is the second in a series of articles dealing with machine learning in asset management.
This article focuses on portfolio weighting using machine learning. Following from the …

This is the first in a series of articles dealing with machine learning in asset management.
Asset management can be broken into the following tasks: (1) portfolio construction, (2) risk …

MTSS-GAN is a new generative adversarial network (GAN) developed to simulate diverse
multivariate time series (MTS) data with finance applications in mind. The purpose of this …

A range of data abstractions have come to the fore since the re-emergence of machine
learning. This includes procedures like feature engineering, extraction, transformation, and …

This thesis focuses on the use of machine learning in financial event prediction. In the past,
finance academics had to be content with mostly linear models that could only ingest a small …

Nonlinear classification models can predict future earnings surprises with a high accuracy
by using pricing and earnings input data. Surprises of 15% or more can be predicted with 71% …

This short report deals with the recent rise of programmatic time series methods. This decade
has witnessed the proliferation of commercial and open source time-series tooling, which …

DataGene is developed to identify data set similarity between real and synthetic datasets as
well as train, test, and validation datasets. For many modelling and software development …