Identifying and mitigating ‘conduct risk’ in algorithmic FICC trading

Abstract

From 31st March, 2021, pursuant to the Senior Managers and Certification Regime (SMCR), British investment firms are required to have performed their first fitness and propriety assessments of persons overseeing the deployment of trading algorithms. Inherent in this requirement is an assumption that natural persons will continue to play the leading role in algorithmic design and calibration for the foreseeable future. Popular non-fiction works), however, are evocative of a future dominated by artificial intelligence with significant levels of autonomy. Building on his recent review of the extant literature concerning conduct risks and their mitigation in algorithmic trading (AT) firms (Culley, 2020), the author proposes a research agenda to assess if initiatives such as the SMCR are considerate of new conduct risks that could emerge in the machine age. Favouring a pragmatic epistemology that the author asserts is consistent with the Financial Conduct Authority’s (FCA) own approach to regulation, the author suggests three possible research strategies (case studies, mixed methods and action research) that could be employed by researchers interested in this topic, before settling on what the author considers to be the most suitable strategy for his own study. In addition, the author provides details on the primary data (elite interviews, focus groups) and secondary data collection techniques that will be utilised, together with an insight into appropriate sampling and data analysis methods. While the author plans to use this research agenda in the context of AT in the fixed income, currencies and commodities markets because of the dearth of studies in this area, it is hoped that it will inspire other scholars to conduct examinations into the conduct risks posed by the automation so that both policymakers and firms can make informed decisions regarding their mitigation in the future.

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ORCID for Alexander Culley: orcid.org/0000-0002-0560-9443

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