Artificial intelligence has recently experienced a remarkable increase in attention, following staggering achievements in applications such as image, text and speech recognition, self-driving cars, or chess and Go tournaments.

It is, therefore, not surprising that the financial services industry is working hard to improve investment decisions by incorporating self-learning algorithms into the investment process. However,

• Do all sectors of the asset management industry exhibit characteristics that can benefit from applying artificial intelligence tools to uncover new patterns?
• Are there limits beyond which additional computing power and greater data availability have only marginal benefits?

In this paper, we will:

• Provide initial answers to the above questions;
• Demonstrate that the adaptivity and self-learning capability of machine learning tools could add value along the entire value chain of an asset manager;
• Examine the fact that the inherently flexible nature of machine learning methods is also the biggest challenge;
• Analyze specific applications of machine learning in quantitative asset management, highlighting limitations, challenges and possible remedies.