This article explores the relationship between bodily rhythms and market rhythms in two distinctly different financial market configurations, namely the open-outcry pit (prevalent especially in the early 20th century) and present-day high-frequency trading. Drawing on Henri Lefebvre's rhythmanalysis, we show how traders seek to calibrate their bodily rhythms to those of the market. We argue that, in the case of early-20th-century open-outcry trading pits, traders tried to enact a total merger of bodily and market rhythms. We also demonstrate how, in the 1920s and '30s, market observers began to respond to a widely perceived problem, namely that market rhythms might be contagious and that some form of separation of bodily and market rhythms might therefore be needed. Finally, we show how current high-frequency trading, despite being purely algorithmic, does not render the traders' bodies irrelevant. Yet high-frequency trading does change the role of the body rather than seeking to attune their bodies to the markets, high-frequency traders seek to calibrate their bodies to their algorithms. While the article demonstrates the usefulness of deploying Lefebvre's rhythmanalysis in analyses of financial markets, it also suggests that high-frequency trading in particular might produce new types of market rhythms that, contra Lefebvre, do not revolve around traders' bodies.

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.

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.

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 (data scientists, software developers, traders, and portfolio managers) 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: (1) an idealistic culture of undivided trust in models, (2) a pragmatic culture of skepticism toward model accuracy, and (3) 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.

Financial contagion is often defined as the propagation of shocks among actors in markets, while excessive correlation and interconnectivity of markets, actors or investment strategies are seen as reasons for its spread. In this article, I examine uses of the concept of contagion across academic, practical and popular discourses on financial markets and speculation from the late nineteenth century through the first couple of decades of the twentieth: During this historical period the concept was frequently used about forms of allegedly irrational behaviour in financial markets. I argue that 'contagion' is used descriptively to capture behaviour and events that escape rational economic explanation and, more importantly, highlights problems of proximity and connectivity in financial markets. While the proximity and connectivity of actors enables market efficiency, they simultaneously increase the risk of contagion. In the latter part of the article, I use a contemporary example of liquidity contagion in model-driven financial investingthe so-called Quant Meltdown of August 2007to emphasise that problems of proximity and connectivity, described as instances of contagion, remain pertinent challenges for market actors to deal with.

This paper contributes to the understanding of the role of crowds in the financial market by examining the historical origins and theoretical underpinnings of contrarian investment philosophy. Developed in non-scientific, practice-oriented 'how to' handbooks in 1920s and '30s America, contrarian investment advice was aimed at so-called small investors rather than well-established market practitioners. Emerging out of late-19th-and early-20th-century debates about public participation in the stock market, the contrarians expanded on a widely held (amongst financial writers) skepticism about the investment and speculation skills (or lack thereof) of the masses and adopted ideas from the theoretical discipline of crowd psychology, whereby they positioned the mass (i.e. the crowd) in opposition to the successful investor. I argue that despite its idiosyncrasies, the contrarians' conception of the market based on crowd psychology, points to a fundamental fragility of market participants, which still lingers on in recent debates about the wisdom of financial market crowds.

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 (a) prospecting (i.e. rendering such data amenable to processing with the aid of analytics tools) and (b) assetization (i.e. the transformation of data into tradable assets). 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.

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