Banking News

FX Markets Efficiency Improved by Machine Learning - BIS

FX Markets Efficiency Improved by Machine Learning - BIS

(20 September 2018 - Global) The rapidly increasing pace of financial markets transaction execution means central banks must continually change and adapt to fulfil responsibilities. 

Increasing trading speeds and the sheer volume of data in electronic markets are challenging central banks to step up their approach to monitoring, in order to satisfy their respective mandates, according to a new report by the Bank for International Settlements (BIS).

The study, “Monitoring of fast-paced electronic markets”, was prepared by a study group led by Imène Rahmouni-Rousseau, director of markets at the Bank of France, and Rohan Churm, head of foreign exchange (FX) at the Bank of England (BoE). The BIS, the global regulator, said the increased use of machine learning in high-frequency FX trading could lead to more efficient markets, particularly the timely incorporation of diverse sources of data in market pricing. Monitoring of fast-paced electronic markets and a thorough understanding of artificial intelligence (AI) and machine learning is increasingly important for the effective monitoring of fast-paced markets and will require a change in risk management techniques. The study highlighted the ‘Brexit’ sterling flash crash event of 7 October 2016 and the flash rally in US Treasuries on 15 October 2014 as demonstrating the importance of close monitoring and timely analysis of high-frequency markets, particularly FX, by central banks.

For example in Spot FX, the share of trading volume executed electronically has almost doubled over the last decade and has become increasingly fragmented across a range of new venues. The report said that more than 70 percent of spot trading is executed electronically since 2013, while an estimated 70 percent of orders on EBS, a primary central limit order book and a major inter-dealer platform for Spot FX, are now submitted by algorithms, and not manually. Most of the machine learning applications in high-frequency trading (HFT) have been focused on equity markets, some on fixed income, while applications in FX are at an early stage. “Nevertheless, research and experimentation is afoot, and lessons from equities will carry over to FX markets,” said the BIS. Some market makers have also used electronic tools to review the profitability and volume of transactions with clients. However the BIS also warned that use of machine learning could push lower-tier banks further towards an agency approach to risk management where they use third-party technologies without sufficient controls and governance. “Multiple participants using similar algorithms simultaneously could lead to herd-like behaviour,” added the report.

The regulator continued that the introduction of the the MiFID II regulations in the European Union (EU) at the outset of 2018 has created a range of new data sources which are being collected by authorities, platforms and data repositories. Although the use of this new data seems promising for the purposes of market monitoring the BIS said there are three challenges:

Spot FX markets are not included in the MiFID II legislation
The frequency of collection is not well defined and there are many exemptions
Lack of centralisation and aggregation of the data

“A major difference is that trade reporting needs to be sent in near real-time and is to be made public, while transaction-level data are highly sensitive and remain non-public,” said the BIS. “After being submitted at a t+1 requirement, transaction data are stored at a very high level of disaggregation.” The study continued that private initiatives to store and make the post-trade data available centrally are emerging in the form of consolidated tape providers. “Such data will probably not be available free of charge and a 100% coverage is unlikely to be achieved,” added the BIS. As a result of the acceleration in electronic trading and fragmentation in trading venues, large quantities of data are created by liquidity providers and trading platforms. The report cited one large bank’s e-trading desk producing around one billion FX quotes per day for clients globally. “In order to deal with these data volumes, firms have begun using cloud services, which reduce the need for hardware and physical data storage,” said the BIS. In addition, technical expertise in programming and analytics will be needed to validate machine learning algorithms. As a result some market participants have highlighted a need to shift to using segregated controls and the ability to limit market access. “Market access controls can act as kill switches, but are usually applied on a graduated basis, imposing limits such as on price or aggregate volume, as well as the number of, duplication and size of orders being sent to trading venues,” said the BIS. “Greater electronification has led to the creation and commoditisation of large quantities of high-frequency data,” added the BIS. “The use of artificial intelligence and machine learning in trading algorithms, while nascent, also has the potential to introduce new market dynamics and increase complexity.”

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