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  1. Introduction: Philosophy for Finance.Emiliano Ippoliti - 2021 - Topoi 40 (4):707-713.
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  • Mathematics and Finance: Some Philosophical Remarks.Emiliano Ippoliti - 2020 - Topoi 40 (4):771-781.
    I examine the role that mathematics plays in understanding and modelling finance, especially stock markets, and how philosophy affects it. To this end, I explore how mathematics penetrates finance via physics, constructing a ‘financial physics’, and I outline the philosophical backgrounds of this process, in particular the ‘philosophy of equilibrium’ and that of critical points or ‘out-of-equilibrium’. I discuss the main characteristics and a few weaknesses of these mathematizations of financial systems, notably econometrics and econophysics, and I compare the two (...)
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  • Model Talk: Calculative Cultures in Quantitative Finance.Kristian Bondo Hansen - 2021 - Science, Technology, and Human Values 46 (3):600-627.
    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 (...)
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  • The Virtue of Simplicity: On Machine Learning Models in Algorithmic Trading.Kristian Bondo Hansen - 2020 - Big Data and Society 7 (1).
    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 (...)
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  • Modeling as a Case for the Empirical Philosophy of Science.Ekaterina Svetlova - 2015 - In Hanne Andersen, Nancy J. Nersessian & Susann Wagenknecht (eds.), Empirical Philosophy of Science. Springer Verlag. pp. 65-82.
    In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of science, namely within the (...)
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