The Absorption and Multiplication of Uncertainty in Machine-learning-driven Finance

Kristian Bondo Hansen*, Christian Borch

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Uncertainty about market developments and their implications characterize financial markets. Increasingly, machine learning is deployed as a tool to absorb this uncertainty and transform it into manageable risk. This article analyses machine-learning-based uncertainty absorption in financial markets by drawing on 182 interviews in the finance industry, including 45 interviews with informants who were actively applying machine-learning techniques to investment management, trading, or risk management problems. We argue that while machine-learning models are deployed to absorb financial uncertainty, they also introduce a new and more profound type of uncertainty, which we call critical model uncertainty. Critical model uncertainty refers to the inability to explain how and why the machine-learning models (particularly neural networks) arrive at their predictions and decisions—their uncertainty-absorbing accomplishments. We suggest that the dialectical relation between machine-learning models’ uncertainty absorption and multiplication calls for further research in the field of finance and beyond.
Original languageEnglish
JournalBritish Journal of Sociology
Volume72
Issue number4
Pages (from-to)1015-1029
Number of pages15
ISSN0007-1315
DOIs
Publication statusPublished - Sep 2021

Bibliographical note

Published online: 27 July 2021.

Keywords

  • Algorithms
  • Economic sociology
  • Financial models
  • Machine learning
  • Uncertainty

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