Machine learning models are becoming increasingly prevalent in algorithmic trading and investment management. The spread of machine learning in finance challenges existing practices of modelling and model use and creates a demand for practical solutions for how to manage the complexity pertaining to these techniques. Drawing on interviews with quants applying machine learning techniques to financial problems, the article examines how these people manage model complexity in the process of devising machine learning-powered trading algorithms. The analysis shows that machine learning (...) quants use Ockham’s razor – things should not be multiplied without necessity – as a heuristic tool to prevent excess model complexity and secure a certain level of human control and interpretability in the modelling process. I argue that understanding the way quants handle the complexity of learning models is a key to grasping the transformation of the human’s role in contemporary data and model-driven finance. The study contributes to social studies of finance research on the human–model interplay by exploring it in the context of machine learning model use. (shrink)

This paper explores how calculative cultures shape perceptions of models and practices of model use in the financial industry. A calculative culture comprises a specific set of practices and norms concerning data and model use in an organizational setting. Drawing on interviews with model users working in algorithmic securities trading, I argue that the introduction of complex machine-learning models changes the dynamics in calculative cultures, which leads to a displacement of human judgment in quantitative finance. In this paper, I distinguish (...) between three calculative cultures: an idealistic culture of undivided trust in models, a pragmatic culture of skepticism toward model accuracy, and a pragmatic idealist culture of early stage skepticism and implementation and production-phase idealism. Based on the empirical material, the analysis engages with examples of each of the three calculative cultures. The study contributes to the social studies of finance and science and technology studies more broadly by showing how perceptions of models shape and are shaped through model work in data-intensive, computerized finance. (shrink)

Since its inception in the late 1970s, behavioural economics has gone from being an outlier to a widely recognized yet still contested subset of the economic sciences. One of the basic arguments in behavioural economics is that a more realistic psychology ought to inform economic theories. While the history of behavioural economics is often portrayed and articulated as spanning no more than a few decades, the practice of utilizing ideas from psychology to rethink theories of economics is over a century (...) old. In the first three decades of the 20th century, several mostly American economists made efforts to refine fundamental economic assumptions by introducing ideas from psychology into economic thinking. In an echo of contemporary discussions in behavioural economics, the ambition of these psychology-keen economists was to strengthen the empirical accuracy of the fundamental assumptions of economic theory. In this article, we trace, examine, and discuss arguments for and against complementing economic theorizing with insights from psychology, as found in economic literature published between 1900 and 1930. The historical analysis sheds light on issues and challenges associated with the endeavour to improve one discipline’s theories by introducing ideas from another, and we argue that these are issues and challenges that behavioural economists continue to face today. (shrink)

This paper has been published in 2015 in Environment and Planning D, 33 : p. 1080–1097. It is freely available from Copenhagen Business School. We thank the authors for the permission to reproduce it here.: This paper explores the relationship between bodily rhythms and market rhythms in two distinctly different financial market configurations, namely the open-outcry pit and present-day high-frequency trading. Drawing on Henri - Management et Business – Nouvel article.

This article examines the historically contentious relationship between the financial market and the public as discussed in academic literature, financial journalism and prescriptive how-to invest handbooks during the late 19th and early 20th centuries. Although financial markets thrive off active public participation, speculating at stock and commodity exchanges has been a sanctioned ritual reserved for a privileged minority. We argue that the financial establishment’s intent to control market access through financial entry-barriers (such as exchange membership fees and margin requirements) has (...) been part of a bigger story we need to understand: a history of delegitimating uninitiated ‘lay speculators’ through the construction of exclusionary narratives about unfit amateur investors and morally corrupt publics. We conceptualize this process as an ongoing and delicate boundary-making exercise contributing to a market participation discourse that has been characterized by a set of reductive binaries, such as those of insider–outsider, professional–amateur and speculator–gambler. We show, however, that attempts to delineate popular participation in financial markets through these binaries have been complicated by the idea that besides being a force of market instability and collective irrationality, the public was a largely untapped source of liquidity. We argue that today’s discourse on public participation in financial markets resuscitates these simplified narratives and propose a more nuanced view of non-professional market participants being both destabilizers and liquidity-providers. (shrink)

Policy discussions and corporate strategies on machine learning are increasingly championing data reuse as a key element in digital transformations. These aspirations are often coupled with a focus on responsibility, ethics and transparency, as well as emergent forms of regulation that seek to set demands for corporate conduct and the protection of civic rights. And the Protective measures include methods of traceability and assessments of ‘good’ and ‘bad’ datasets and algorithms that are considered to be traceable, stable and contained. However, (...) these ways of thinking about both technology and ethics obscure a fundamental issue, namely that machine learning systems entangle data, algorithms and more-than-human environments in ways that challenge a well-defined separation. This article investigates the fundamental fallacy of most data reuse strategies as well as their regulation and mitigation strategies that data can somehow be followed, contained and controlled in machine learning processes. Instead, the article argues that we need to understand the reuse of data as an inherently entangled phenomenon. To examine this tension between the discursive regimes and the realities of data reuse, we advance the notion of reuse entanglements as an analytical lens. The main contribution of the article is the conceptualization of reuse that places entanglements at its core and the articulation of its relevance using empirical illustrations. This is important, we argue, for our understanding of the nature of data and algorithms, for the practical uses of data and algorithms and our attitudes regarding ethics, responsibility and regulation. (shrink)

Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative (...) data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of prospecting and assetization. We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues. (shrink)