Trading Beyond Understanding

Regular price €28.50
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
A01=Christian Borch
AI
Author_Christian Borch
Automated trading
Category=JHBA
Category=KCP
Category=KFFM
Category=PDR
Category=UYQM
Economic sociology
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_new_release
eq_nobargain
eq_non-fiction
eq_science
eq_society-politics
Human-machine interaction
Machine learning
Risk
Social theory

Product details

  • ISBN 9781503645523
  • Dimensions: 152 x 229mm
  • Publication Date: 10 Mar 2026
  • Publisher: Stanford University Press
  • Publication City/Country: US
  • Product Form: Paperback
Secure checkout Fast Shipping Easy returns

Machine learning is fundamentally transforming financial markets. Where trading strategies were once crafted by human experts—executed manually or through pre-coded rules—firms now build models that generate the strategies themselves. These are not just tools but trading automatons: semi-independent systems designed to learn from markets and act on their own. Drawing on over a decade of fieldwork in financial markets, Christian Borch offers a rare inside look at how these systems are built, the risks they pose, and how they challenge our understanding of markets and decision-making. As trading automatons grow more complex and opaque—even to their designers—new sociological questions emerge: What happens when machines become the primary agents in markets? And how should we understand economic action when human judgment is no longer at the center? Trading Beyond Understanding is a powerful investigation of machine agency, market transformation, and the shifting boundaries between technological systems and social life.

Christian Borch is Professor of Sociology at the University of Copenhagen. He is the author of The Politics of Crowds (2012) and Social Avalanche (2020), and co-editor of The Oxford Handbook of the Sociology of Machine Learning (2025).

More from this author