The absorption and multiplication of uncertainty in machine‐learning‐driven finance
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The absorption and multiplication of uncertainty in machine‐learning‐driven finance. / Hansen, Kristian Bondo; Borch, Christian.
I: British Journal of Sociology, Bind 72, Nr. 4, 2021, s. 1015-1029.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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 -
ID: 319888726