Artificial intelligence for asset allocation

Petter N. Kolm is a professor at the Courant Institute, New York University (NYU), USA. Thanks to a grant from Knut and Alice Wallenberg Foundation, he will be a visiting professor at Centre for Mathematical Sciences, Lund University.

Artificial intelligence, or AI, has already changed vast areas of society. Autonomous computer systems can now drive cars, beat humans at chess and diagnose diseases. However, in some fields, applications have been delayed because computers are not entirely reliable – even well-functioning AI systems can, in practice, be useless if they cannot be fully trusted. 

This project focuses on the development of AI for quantitative finance and asset allocation, a field that has been hesitant about using AI technologies because even very small errors can lead to huge financial losses.

Two problems will be studied. In the first step, data-driven methods for finding abrupt changes to asset dynamics will be developed. The difficulty here is in selecting the few relevant variables among hundreds or thousands of others that simply add noise. The next step involves combining the data-driven method with a machine-learning method called reinforcement learning. Reinforcement learning’s biggest successes include the AlphaGo software, which defeated the world champion at the Go. Go is perhaps the world’s most complex board game, with up 10³⁶⁵ possible board positions. 

Reinforcement learning has the advantage that it can be broadly employed, thus allowing solutions for considerably more realistic problems than in traditional methods for machine learning. For example, AI-based asset allocation could be of great importance for pension funds and private insurance foundations.