Hoping that the promises of machine-learning can be realised in financial markets, investment management and trading firms increasingly employ machine-learning techniques to extract exploitable informational edge from large datasets. In addition to heavy investments in technology and the human resources capable of manipulating it, this development has led to increased use of open-source machine-learning and data-management resources. Drawing on 44 interviews with developers and users of machine-learning techniques in finance, we explore how such platforms and other open-source resources are understood and used by said practitioners. Building on work in the Social Studies of Finance (SSF) on financial modelling and platformisation, we argue that these users of machine learning in finance engage in what we term stack bricolage activities, when they reuse disparate open-source resources in their modelling work. We argue that stack bricolage creates dependencies on open-source cloud resources characterised by infrastructural impermanence, which is a result of their substitutability and maintenance sensitivity. Our study contributes to the emerging SSF literature on machine-learning modelling cultures and debates in Science and Technology Studies and adjacent fields on the reuse of data and software in platformised cloud infrastructures.
Bibliographical noteEpub ahead of print. Published online: 24 August 2023.
- Financial markets
- Infrastructural impermanence
- Stack bricolage