Shopping Cart -

Your cart is currently empty.
Continue Shopping
This website use cookies and similar technologies to improve the site and to provide customised content and advertising. By using this site, you agree to this use. To learn more, including how to change your cookie settings, please view our Cookie Policy
Pocketmags Digital Magazines
Pocketmags Digital Magazines
   You are currently viewing the Australia version of the site.
Would you like to switch to your local site?
Digital Subscriptions > The Hedge Fund Journal > Issue 130 – March 2018 > Artificial Intelligence

Artificial Intelligence

Chances and challenges in quantitative asset management

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 also the financial industry is ever more heavily trying to improve investment decisions by incorporating self-learning algorithms into the investment process. For that matter, the application of quantitative tools and algorithms in order to define systematic trading strategies has already a strong history in the hedge fund industry. Against this backdrop, quantitative hedge funds may provide a fertile soil for the application of new machine learning techniques. But do all sectors of the asset management industry exhibit characteristics that can be exploited by artificial intelligence tools to uncover new patterns? What could be the especially relevant fields? Are there limits beyond which additional computing power and greater data availability have only marginal benefits? This research note provides some initial answers. It shows that the adaptivity and self-learning capability of machine learning tools could add value along the entire value chain of an asset manager. However, the inherently flexible nature of machine learning methods is also the biggest challenge. These methods must be applied thoughtfully and in the right context. We start with a general overview of machine learning, then elaborate on specific applications in quantitative asset management, highlighting the limitations, challenges and possible remedies before reaching our conclusions.

From machine learning in general …

Machine learning refers to extracting knowledge from data by identifying correlated relationships without receiving prior information about what causal dependencies to look for. It combines elements from both statistics and computer science and has been in existence for many years. As early as 1956, John McCarthy at a conference on the campus of Dartmouth College coined artificial intelligence as “the science and engineering of making intelligent machines”. However, it is mostly due to recent significant advancements in computing power and data availability that the application of artificial intelligence algorithms has become relevant in everyday life.

Purchase options below
Find the complete article and many more in this issue of The Hedge Fund Journal - Issue 130 – March 2018
If you own the issue, Login to read the full article now.
Single Issue - Issue 130 – March 2018
Or 17999 points
6 Month Digital Subscription
Only $ 150.00 per issue
Or 74999 points

View Issues

About The Hedge Fund Journal

Informing the Hedge Fund Community. With access to some of the industry’s biggest names and an astute and talented group of writers and contributors, The Hedge Fund Journal has established itself as a trusted source of information on the hedge fund industry.