Curating Independent Machines: How Machine Learning Reshapes Financial Markets

Name of applicant

Christian Borch




University of Copenhagen


DKK 1,058,697



Type of grant

Monograph Fellowships


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.

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