TY - JOUR

T1 - The absorption and multiplication of uncertainty in machine‐learning‐driven finance

AU - Hansen, Kristian Bondo

AU - Borch, Christian

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Faculty of Social Sciences

KW - algorithms

KW - economic sociology

KW - financial models

KW - machine learning

KW - uncertainty

U2 - 10.1111/1468-4446.12880

DO - 10.1111/1468-4446.12880

M3 - Journal article

C2 - 34312840

VL - 72

SP - 1015

EP - 1029

JO - British Journal of Sociology

JF - British Journal of Sociology

SN - 0007-1315

IS - 4

ER -