Coping with Messiness and Fogginess in Financial Information Management: Material and Social Aspects of Representations in Proprietary Trading and Custodial Services

Chapter

Abstract

The aim of this chapter is to connect the representational theory of financial records with concepts related to the interplay between sociality and materiality in the techno-organizational settings of financial operations. At a second stage the aim is to apply this conceptual framework to the analysis of two critical operational domains: proprietary trading and custodial services. Proprietary trading is driven by fogginess of information resulting from blackboxing as a way to cope with capital market and operational complexities, as well as with time pressures. On the contrary, custodians struggle to restore the visibility of financial records. Furthermore, the awareness of messiness and fogginess, as central components of information management in finance, is related to the informational consequences of financial bubbles originating especially from high-leverage and derivative-driven operations. Cognitive framing, adopted by professional groups and communities of practice in financial organizations, often results in foggy representations that obstruct the view of the primary sources of information stored on financial records. Institutionalized and formalized expertise can under certain circumstances cause fogginess and hinder reflexive approaches regarding the way recorded information is shaped and used. Fogginess implies also messiness of entries and of data organization as a consequence of reduced visibility of the features of the records and of the subsequent inscriptional requirements. This analytic perspective can be applied beyond proprietary trading and custodial services, to other communities of practice in finance facing problems of information management and thus exposed to high operational risks.

Keywords

Financial Organization Hedge Fund Financial Technology Financial Instrument Record Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Political Science and Public AdministrationUniversity of AthensAthensGreece

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