Temporal Structures of Algorithmic Sensemaking: The Case of Financial Markets

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    This paper examines the temporal structure that trading algorithms in financial markets enact to develop and execute decisions. The data show that while these algorithms inherit certain characteristics of the traditional sensemaking carried out by human members of organizations, algorithmic sensemaking is considerably independent of the organizational time and context. Financial market participants often construct a variety of temporal structures to develop optimal algorithms that generate signals or cues from the environment. Further, to navigate the complex environment, algorithmic sensemaking in financial markets enacts a separate framework and process as well as dynamic temporalities for the action. The three temporalities converge in the present, when the actions occur, as the decisions from the past are translated into detailed execution decisions based on the current environment and post-action predictions about the future state of the market.
    Original languageEnglish
    Publication date2018
    Number of pages30
    Publication statusPublished - 2018
    EventFutures of Finance and Society 2018: Third Annual Conference of the Finance and Society Network - University of Edinburgh, Edinburgh, United Kingdom
    Duration: 6 Dec 20187 Dec 2018
    Conference number: 3


    ConferenceFutures of Finance and Society 2018
    LocationUniversity of Edinburgh
    Country/TerritoryUnited Kingdom
    Internet address

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