Temporal Structures of Algorithmic Sensemaking: The Case of Financial Markets

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.