The Fixed Income, Currencies and Commodities (FICC) Markets Standards Board (FMSB) has published a review of emerging challenges in algorithmic trading and machine learning, warning of risks to market stability from unsupervised models.

The report, written by FICC data and technology advisor Rupak Ghose, noted that the application of model risk management to algorithmic trading is an area that has received less attention.

“Historically, algorithmic trading has been most prominent in highly liquid markets, which have significant amounts of high-quality data,” he wrote. “As the application of algorithms has expanded into less liquid products and with increased utilisation of new machine learning techniques, the challenges of securing the quality and consistency of data needed are self-evident.”

But perhaps less obvious is the need to manage for increased model risk, stated Ghose, adding that in the near term, machine learning in wholesale FICC markets looks likely to remain restricted to specific minor functions only and as a relatively small part of the overall trading and reporting process with tight controls in place.

“As in other businesses where machine learning is being adopted, there are nascent concerns about the conduct risks that might crystallise as a result of unintended design flaws, implementation and use,” read the report. “There is also increasing discussion within the industry about practices that can mitigate any market abuse or stability risks that may emerge.”

Algorithmic trading is increasingly regulated in major global financial centres. In the UK for instance, both the Financial Conduct Authority and the Prudential Regulation Authority have issued supervisory guidelines relating to governance, algorithm approval processes, testing and deployment, documentation of algorithms, and risk controls.

“Significant risks arise from the failure of systematic or operational controls that are intended to prevent or limit loss exposure for highly automated transactions,” stated Ghose. “System runaway issues have the potential to cause material losses in a short period of time and the lack of a robust software development lifecycle process was cited as the main cause of high-profile incidents in recent years such as seen at Knight Capital.”

The other regulatory focus has been on conduct and the risk of algorithmic strategies being coded, or learning, to disadvantage clients, abuse markets or cause disorderly markets.

Mark Yallop, chair of FMSB, commented: “This is a space that is developing fast and creating exciting opportunities in markets, but also an emerging area of risk and vulnerability.

“We hope this review will create further discussion on the nascent challenges market participants face and also inform potential topics for FMSB’s future work.”

Ciara Quinlan, global head of principal electronic trading, rates and credit at UBS - and an FMSB Standards Board member - said: “As the adoption of algorithmic trading expands into new products and new machine learning technologies emerge, model risk is likely to become increasingly relevant.”

The review is the first in a series that will collectively consider issues of FICC market structure and the impact of regulatory and technological change on the fairness and effectiveness of wholesale markets. The next publication will cover the role of data management in the financial system.

FMSB members are advancing a statement of good practice on algorithmic trading, noting that the topic will remain an important focus for transparency, fairness and effectiveness of trading practices in the coming years.

Share Story: