New York Fed: “High-frequency trading (“HFT”), or high-speed trading (“HST”), a type of algorithmic (or “algo”) trading, is now a well-known feature of the global market landscape. In many markets, a small number of firms may account for a large proportion of trading volume. Although it has been argued that HFT has lowered investors’ trading costs by reducing bid-ask spreads, the risk that HFT activity specifically, and algorithmic trading more generally, poses to firms and the financial markets has sparked debate and raised concern among market participants and regulatory agencies globally. This is, in part, owing to the speed of trading and, therefore, the pace at which exposures may accumulate intraday at financial institutions. Indeed, unexpected events linked to algorithmic and high- frequency trading have caused significant volatility and market disruption, leading to heightened debate around the risks these activities pose to the functioning of global markets. The complexity of market interactions among HFT firms and other market participants increases the potential for systemic risk to propagate across venues and asset classes over very short periods of time. This briefing note focuses on how risks associated with algorithmic trading are monitored and controlled at large financial institutions during the trading day. While market structure and trading rules differ by jurisdiction and asset class, we seek to identify risks common to algorithmic trading and to suggest questions that supervisors might consider as they monitor or examine this activity. Further, by setting forth risk-based principles and questions that firms already engaged in algorithmic trading can use to assess their controls over this activity, we aim to facilitate an informed conversation about sound risk management practices and renew market participants’ focus on improving risk management of this activity. Key supervisory concerns center on whether the risks associated with algorithmic trading have outpaced control improvements. The extent to which algorithmic trading activity, including HFT, is adequately captured in banks’ risk management frameworks, and whether standard risk management tools are effective for monitoring the risks associated with this activity, are areas of inquiry that all supervisors need to explore. Further, algorithmic trading activity has expanded beyond the U.S. equity markets to other markets and asset classes, including futures, foreign exchange, and fixed-income markets. Thus, our supervisory approach needs to remain flexible and adaptable to address growth and evolution of this activity.”