Curating Independent Machines: How Machine Learning Reshapes Financial Markets
Name of applicant
Christian Borch
Title
Professor
Institution
University of Copenhagen
Amount
DKK 1,058,697
Year
2022
Type of grant
Monograph Fellowships
Summary
The financial markets have been widely automated during the past few decades, and most orders to buy or sell stocks and other securities are now placed by fully automated algorithms. These automated trading systems used to be “human-defined,” meaning that the strategies they pursued were conceived of by humans. Since the mid-2010s, however, a new type of algorithmic systems has become popular—automated trading systems based on so-called machine learning techniques. These systems are designed to generate their own strategies independently of human input. The aim of this project is (1) to examine this transformation toward machine-learning-based automated trading and its consequences for market risk as well as (2) to analyze its broader sociological implications.