Viewpoint: Rating changes increase appetite for trading desk data

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Dan Barnes asks Emil Parmar, Director of Credit Trading Solutions at London Stock Exchange Group how the better provision of analytics drives more effective trading as bond markets see risks rising.

The liquidity drought in 2022’s market will be exacerbated by rising defaults. Emil Parmar of London Stock Exchange Group explains how for bond traders, having real time awareness of macro, credit and market data will become invaluable when delivering best execution.

What have traders been dealing with in 2022’s bond markets?

Liquidity has been scarce, while bond funds have managed outflows and dealt with a slowdown in new issues. There’s been an incredible amount of attention focused on the US Federal Reserve (The Fed), given its hawkish tilt, as we have watched credit spreads widen and returns being challenged. Days have been rather choppy with the high volatility and a mixed bag of investors who continue to stay risk-off.

Central banks winding down buybacks and dealers cautiously managing inventory will have consequences on levels and direction of trading activity.

These factors along with credit risks increasing, can increase volatility further. If bonds or even sectors are significantly re-rated, that will affect which assets can be carried within a given portfolio, based on its mandate.

How are traders responding to these market conditions?

In volatile markets, the window to execute a trade effectively is often smaller. That accelerates the rate at which traders and portfolio managers (PMs) need to communicate and confirm a trade. The more informed they are at each point, the faster they can make that decision. 

It is also important to be aligned with the quality and frequency of the information – maybe the PM is referring to an end of day close price, whereas the trader is observing an intraday snap or real-time composite.

Not all composites are the same however – depending on the sector, whether trading short-end or long-end, or high yield credit in developed markets vs emerging markets, information needs to be fine-tuned to give the desk the best/most competitive information possible. 

Coverage for pricing itself has become less of an issue – now it is about quality and access to markets which entails being open-minded to new trading protocols. The same can be overlaid when referencing liquidity scores or measuring distance for bid/ask spreads. It is also important that critical functions of the wider trading team are aligned when observing market conditions, such as market risk managers.

Are trading desks all affected in the same way?

A sell-off, as we’ve seen this year, will affect different parts of the investment or execution workflow depending on which side of the trade you are on. Trade velocity, for example, is a sharp focus for all sell-side systematic trading desks – it is important to respond to the general growth in electronic inquiry volume while maintaining balance sheet efficiency.

So trading the ‘right bonds’ is crucial and doing so in concert with the traditional principal trading desks who need to manage inventory risk. There is little margin for error, which has been especially difficult when we have seen net asset values (NAVs) for bond exchange-traded funds (ETFs) gap to historic levels – even the most efficient workflows are being stressed.

For real-money accounts, macroeconomic changes will force thematic shifts to a portfolio that needs to be managed, but without overpaying liquidity premiums. Hedge funds on the other hand have the option to toggle risk on or off in volatile markets but need to have confidence in the information leading to trade decisions. While the effects may differ between trading desks in these markets, there is a common thread for the need to enhance workflows and integrate data where it is needed the most.

How does that affect the use of data by traders specifically?

When price moves are unpredictable and amounts outstanding can vary, buy-side traders often need greater discretion on which bonds can be bought or sold, with the present challenges in sourcing liquidity. Awareness of comparable bonds that provide the same investment objectives, but are liquid, is invaluable.

Traders need to support the decision-making process that the portfolio manager will go through, and so they need to consider price and ability to execute, but also term sheets and covenants that might be relevant. The decisions made to invest at the time of issuance may differ when trading in the secondary market. The pandemic has presented some interesting swings – it is possible that a bond with flexibility to raise additional secured debt is favoured more highly than when it hit the primary market prior to Covid. More, higher quality data that can be delivered in context of the trade opportunity helps to make the strongest execution decisions. 

What do they need to do, in order to see that information?

Firstly, get access to the right data. The right sources need to be identified, and the data tested and cleaned – normalisation remains a tricky, although not an impossible challenge. Then, that data needs to be brought into an analytics package which provides the right view of historic and forward-looking information that can be seen across different functions. Next comes the interoperability of this data – traders need to build context and develop themes across different applications with the fewest clicks possible.

What are the barriers to accessing information?

Data can be from multiple providers so aggregating it can be a challenge. Building comprehensive analytics for an array of scenarios is also difficult. In fact, often the data is ‘there’, but it lives on an application programming interface (API) feed or a data-lake in a cloud. Traders can many times be the strongest subject matter experts, however, they do not always have the luxury of penning down requirements to instruct how the information needs to be visualised and interacted with when it lives in different places. It is no surprise that coding skills such as Python are sought after on trading desks these days, but the tools to simplify workflows still need to be improved.

While significant progress has been made to implementing cloud computing strategies, for many global trading firms, the data architecture and infrastructure can be complex.

How can these barriers be overcome?

Data needs to be brought into the workspace, as seamlessly as possible. To do that well, we’re seeing data architecture and strategies evolve as the complexity and demands do as well. Not everything lives in a cloud. It’s not always that simple.

In fact, as much as a centralised ‘golden source’ can be of value, additional value can be unlocked by implementing a data mesh – a planned decentralised data framework. The right tools that allow the transfer from source to system can make all the difference. Having a multi-faceted and sometimes open framework approach to managing data can grant flexibility that is much needed in the institutional space.

Connectivity between portfolio, order and execution management systems, integrating data providers, along with the use of desktop interoperability tools bring us into a new age of the investment professional. These are allowing desks to better integrate best-of-breed providers and create a single view of the market, with alerts to trigger greater awareness of information.

The combined effort to reduce clicks on the desktop, by way of low-code/no-code solutions, and normalising content across new and legacy infrastructure, all begin to lower the barriers to accessing information that promotes the best investment decisions with ease.

What advantages will buy-side traders see through better deployment of data?

As workflows compress, it will be helpful for a trader to have visibility as to what their real time engagement is with their counterparties. This includes working closely and effectively with dealers as the inventory and holdings landscape continues to change.

Analysing best execution in equities is far more quantifiable, but that analysis is not as common in fixed income. Where bond traders need to layer in qualitative assessments to deliver best execution for clients, they still need to pull together data, to assess how to manage a trade.

Their decisions may not be automated, but they will be faster, and their portfolio managers’ investment objectives will be executed more effectively. 

©Markets Media Europe, 2022
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