FMSB Algo Trading Guidance is First Step to Best Practice Standard Wednesday, July 15, 2020 Regulatory Intelligence The FICC Markets Standards Board issued a statement of good practice on algorithmic trading. As algo trading becomes more prevalent, regulators are keen to lay down some guidelines and this is the aim of this progressive market trade association. It promotes governance and conduct across all FICC asset classes. The statement comprises 10 good practice statements to help govern and control conduct risk, especially in less regulated asset classes and markets. The FMSB has taken the lead in trying to define acceptable market behavior and note instances where poor conduct is evident that affects a market’s reputation. The statements recommend a proper governance framework for algo trading that includes senior management supervision with an appropriate escalation procedure, as well as established lines of responsibility in the second and third line of defense to provide independent oversight. They call for a list and description of all algos in use such that these can be understood by management, the second line, and regulators. This is essential practice and needs to be applied across the application of AI in financial services to diminish the “black box” effect. Risk control is covered, suggesting the use of specialist second line supervisors, as well as sharing information across asset classes where a conduct breach has occurred. Firms should map their standard risk assessment approach to their algo trading to achieve equivalence. There should be a process required for development change of each algo, including testing in an environment resembling production before the code is deployed. An audit trail for the changes should be present. The statements demand holistic oversight to identify and manage the risks in algo trading. There should be monitoring of messages between the trading entity and the trading venue to identify any market abuse. The statements are packed with practice guidance that all operators of algorithms should adhere to as the world seeks more transparency in a field of data science that is a growing influence on everything we do. They call for significant engagement and oversight from the second line of defense which will no doubt become the expectation of supervising regulators when they come to examine a firm’s electronic and algo trading practice. It sets out a robust set of practices that would stand any organization in good stead when the regulator comes knocking.