The day to day role of the repo trader has changed rapidly in the past 10 years, driven by a number of market and regulatory trends. More radical change is on the horizon with the advent of artificial intelligence, machine learning, and predictive analytics. A guest post from Jerome Cardon and Martin Seagroatt of Broadridge.
First, the continuing low interest rate environment, coupled with a long list of regulations that increase the cost of trading (e.g. leverage ratio, capital costs, SFTR to name but a few) resulted in a squeeze on the profitability of the repo desk. Those running the repo function found they were suddenly required to do more with less, so efficiency gains, particularly through technology, became a key focus as a way to improve margins.
Moreover, following the LIBOR scandal, it became apparent that OTC voice based trading, driven by an opaque web of market relationships was a source of regulatory risk for market participants. This led to a desire among market participants and regulators for more electronic trading, offering greater transparency and audit trails of activity, and reducing the scope for collusion. It also provides more visible proof of best execution.
The trends discussed above have driven increased electronification of repo markets. There has been some fall-back in this trend, due to the bond market squeeze experienced towards the end of 2016 in Europe. This highlighted the importance of maintaining a relationship focussed approach to sourcing liquidity when times of market stress occur. Despite this, there remains a long term trend towards trading via electronic platforms.
This increase in electronic trading has occurred in combination with a growing overlap of electronic repo markets, good-to-trade brokerage platforms such as LSEG’s MTS and Nex’s BrokerTec, resulting in added complexity for the repo trader. The increased scope of the various electronic trading platforms, and idiosyncrasies between how each broker presents data through its user interface, creates major inefficiencies and can result in missed trading opportunities; traders need to rapidly identify and quote prices across multiple markets in seconds, rather than wasting valuable minutes surveying multiple venues trying to make sense of heterogeneous data.
The increased scope of the repo desk
Another major trend is the convergence between global inventory management and trade execution. Basel III regulation and the direct link it has created between capital and funding has made repo a critical instrument in managing down the cost of other activities, directly in the case of derivatives activities, or indirectly for hedges. This means the desk frequently involves disciplines such as collateral optimisation, transformation, and firm balance sheet management in addition to its traditional filling shorts and financing role.
Increasing the efficiency and effectiveness of the repo trader
This combination of fewer resources, increased scope, growing complexity, and fragmentation of the market landscape has resulted in a need to assess how repo traders can become more effective and efficient in response. We identify three key areas for improvement; data aggregation/normalisation, automation, and analytics.
Data aggregation and normalisation
There is a growing need to normalise data from different trading venues in a single screen. This ensures traders do not waste valuable time and mental capacity trying to aggregate liquidity manually across multiple markets in different formats. A single market view provides benefits in this respect.
When this view is combined with an integrated view of global inventory, assisting with general collateral (GC) allocations and clearly showing over term and over basket definitions, the trader can then bring inventory to market more easily or address the firm’s financing needs or short positions more rapidly.
Automating many of the time consuming and mundane manual tasks traders need to carry out on a day to day basis can also offer significant scope for efficiency improvements and frees up time for decision making. For example, manually populating 50 lines of securities with 50 separate quotes is not the best use of a trader’s time. Using process automation solutions for bulk quoting and market sweeps can reduce some of these bottlenecks and automate the trader’s day to day activities to the greatest degree possible.
The data aggregation/normalisation and automation described above begin to open possibilities around pre-trade data analytics and decision support tools. With the advent of artificial intelligence (AI), machine learning, and predictive analytics, we should see the emergence of mainstream solutions that can analyse real-time market trends and firm inventory.
Traders and desk heads will still establish objectives and binding constraints, but a future AI driven system could then suggest integrated transaction combinations to meet those goals that are more complex than a basic single trade approach.
Firms could also deploy predictive analytics to determine when prices diverge from general collateral (GC) rates or when liquidity will be traded, and where. From a pricing perspective, the optimal time to trade can be informed by the ability to use big data to identify correlations between a wider range of market and other news data and their impact on peaks in liquidity, or when spreads are compressed.
The trader can then add an element of human strategic thought around how their trading activities impact the various parameters the firm is trying to optimise for, before they then execute these trading activities with a minimum number of manual touchpoints. This sanity checks the actions suggested by the AI algorithm and offers the ability to overlay an aspect of human creativity across a machine driven approach to trading.
While the day to day life of the repo trader is becoming more complex, a number of tools are available to help traders deal with this added complexity, while also opening up the ability to automate manual tasks and enhance trading opportunities. Firms that utilise the latest technology, such as Broadridge’s front office Repo Order Quote (ROQ) solution to aggregate electronic markets, integrate global inventory, and automate execution will unlock business efficiencies and increase revenue opportunities. As repo becomes more central to core competencies such as collateral optimisation, liquidity, and balance sheet management for financial firms, the importance of effective repo trading has never been greater.
About the Authors
Jerome Cardon is Repo Product Manager at Broadridge. Jerome has over 20 years of experience gained in both the Consultancy and Investment Banking industries, including senior level roles at Lloyds, RBS, Capco, ING and Deutsche Bank. Jerome has a specific focus on risk management, regulatory compliance and change management and in a previous role undertook the design and delivery of CVA-FVA, RWA, and Collateral Optimisation desks for a European financial institution.
Martin Seagroatt is the Global Marketing Director for Broadridge’s securities financing and collateral management business unit. Previously he was Marketing Director at 4sight Financial Software. In June 2016 4sight was acquired by Broadridge and the 4sight Securities Finance and Collateral Management System rebranded as the Broadridge Securities Financing and Collateral Management Solution. In his 12 years in the industry he has specialised in securities lending, repo, and OTC/listed derivatives collateral management solutions. Prior to that Martin worked as a business expert in technology systems for risk management in the energy industry.