This paper provides a review on machine learning methods applied to the asset
management discipline. Firstly, we describe the theoretical background of both machine …

Financial risk management avoids losses and maximizes profits, and hence is vital to most
businesses. As the task relies heavily on information-driven decision making, machine …

The tremendous speedup in computing in recent years, the low data storage costs of today,
the availability of “big data” as well as the broad range of free open-source software, have …

This systematic literature review analyses the recent advances of machine learning and
deep learning in finance. The study considers six financial domains: stock markets, portfolio …

Recent advances in machine learning are finding commercial applications across many
industries, not least the finance industry. This paper focuses on applications in one of the …

In this work, an innovative approach to exploit the true potentials of machine learning (ML)
by the financial industry is presented; using ML technology not as a source of investment …

This new edited volume consists of a collection of original articles written by leading
financial economists and industry experts in the area of machine learning for asset …

Purpose Machine learning (ML), and deep learning in particular, is gaining traction across a
myriad of real-life applications. Portfolio management is no exception. This paper provides a …

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 …

The rapid development of financial technology not only provides a lot of convenience to
people's production and life, but also brings a lot of risks to financial security. To prevent …