Expectations, competencies and domain knowledge in data- and machine-driven finance

被引:4
|
作者
Hansen, Kristian Bondo [1 ]
Souleles, Daniel [2 ]
机构
[1] Copenhagen Business Sch, Dept Management Soc & Commun, Copenhagen, Denmark
[2] Copenhagen Business Sch, Dept Business Humanities & Law, Copenhagen, Denmark
基金
欧盟地平线“2020”; 欧洲研究理事会;
关键词
Domain expertise; expectations; financial markets; machine learning; skills; work; SOCIOLOGY; TECHNOLOGY; SCIENCE; FUTURE; WORK; DYNAMICS; STORIES; ENERGY;
D O I
10.1080/03085147.2023.2216601
中图分类号
F [经济];
学科分类号
02 ;
摘要
Expectations about the economy and financial markets are often cast as figments of imaginaries of the future. While the sociology of finance have predominantly dealt with expectation formation in relation to calculative devices used in practices of valuation and prediction, this paper concerns the expectations finance professionals form about their work in data- and machine-driven finance. We examine how high-skilled professionals reflexively form expectations about their work and argue that techno-centric imaginaries of the future of finance tend to create an emphasis on domain-independent data science skills over financial domain knowledge. However, we show that such imaginaries do not necessarily perform the work-related expectations of financial professionals, but are instead challenged and nuanced in reflections about the value of practice-bound domain knowledge and expertise.
引用
收藏
页码:421 / 448
页数:28
相关论文
共 50 条
  • [1] Optimal machine-driven acquisition of future cosmological data
    Kostic, Andrija
    Jasche, Jens
    Ramanah, Doogesh Kodi
    Lavaux, Guilhem
    ASTRONOMY & ASTROPHYSICS, 2022, 657
  • [2] A data- and knowledge-driven framework for developing machine learning models to predict soccer match outcomes
    Berrar, Daniel
    Lopes, Philippe
    Dubitzky, Werner
    MACHINE LEARNING, 2024, 113 (10) : 8165 - 8204
  • [3] Spatial modelling of disease using data- and knowledge-driven approaches
    Stevens, Kim B.
    Pfeiffer, Dirk U.
    SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY, 2011, 2 (03) : 125 - 133
  • [4] Diagnostics and Prognostics of Boilers in Power Plant Based on Data- Driven and Machine
    Widodo, Achmad
    Prahasto, Toni
    Soleh, Mochamad
    Nugraha, Herry
    INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT, 2025, 16 (01)
  • [5] A data- and knowledge-driven framework for digital twin manufacturing cell
    Zhang, Chao
    Zhou, Guanghui
    He, Jun
    Li, Zhi
    Cheng, Wei
    11TH CIRP CONFERENCE ON INDUSTRIAL PRODUCT-SERVICE SYSTEMS, 2019, 83 : 345 - 350
  • [6] Machine Learning for Mapping and Forecasting Poverty in North Sumatera: A Data- Driven Approach
    Arnita
    Arpaung, Faridawaty m
    Amadhani, Fanny r
    Inata, Dewan
    SAINS MALAYSIANA, 2024, 53 (07): : 1715 - 1728
  • [7] Data- and knowledge-driven mineral prospectivity maps for Canada's North
    Harris, J. R.
    Grunsky, E.
    Behnia, P.
    Corrigan, D.
    ORE GEOLOGY REVIEWS, 2015, 71 : 788 - 803
  • [8] CLEP: a hybrid data- and knowledge-driven framework for generating patient representations
    Bharadhwaj, Vinay Srinivas
    Ali, Mehdi
    Birkenbihl, Colin
    Mubeen, Sarah
    Lehmann, Jens
    Hofmann-Apitius, Martin
    Hoyt, Charles Tapley
    Domingo-Fernandez, Daniel
    BIOINFORMATICS, 2021, 37 (19) : 3311 - 3318
  • [9] Data- and interaction-driven approaches for sustained musical practices with machine learning
    Vigliensoni, Gabriel
    Fiebrink, Rebecca
    JOURNAL OF NEW MUSIC RESEARCH, 2025,
  • [10] Combining Data- and Knowledge-Driven AI with Didactics for Individualized Learning Recommendations
    Landes, Dieter
    Sedelmaier, Yvonne
    Boeck, Felix
    Lehmann, Alexander
    Fraas, Melanie
    Janusch, Sebastian
    2024 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE, EDUCON 2024, 2024,