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.

中文翻译：

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机器学习驱动金融中不确定性的吸收和倍增

市场发展的不确定性及其影响是金融市场的特征。机器学习越来越多地被部署为一种工具来吸收这种不确定性并将其转化为可管理的风险。本文通过对金融行业的 182 次采访，包括 45 次对积极将机器学习技术应用于投资管理、交易或风险管理问题的线人的采访，分析了金融市场中基于机器学习的不确定性吸收。我们认为，虽然机器学习模型被用于吸收金融不确定性，但它们也引入了一种新的、更深刻的不确定性，我们称之为关键模型不确定性。关键模型不确定性是指无法解释机器学习模型（尤其是神经网络）如何以及为什么得出其预测和决策——它们吸收不确定性的成就。我们建议，机器学习模型的不确定性吸收和乘法之间的辩证关系需要金融领域及其他领域的进一步研究。