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Bloomberg: Better execution through bond trading analytics

  • June 20, 2022

Will Oberuch, Global Head of TCA Product at Bloomberg.

Will Oberuch, Global Head of TCA Product at Bloomberg.

The DESK: Where do you see Bloomberg’s trading analytics being most effective today?

Will Oberuch: Our ability to support trading across the multi-asset framework is a key strategy, and a strength. Initially our transaction cost analysis tools were predominantly used post-trade, assessing dealer performance up to trading desk performance, and as granular as trader-level performance. What we're seeing now is a transition into the trading workflow, taking post-trade analysis and presenting that information in a way that helps provide intelligence before a trade is executed.

TD: Does that require close integration with execution management systems and order management systems?

WO: Our own EMS has provided trading analytics for equities for several years, specifically with our pre-trade cost model, which has been well received by clients. Within the EMS, clients can configure columns plus a trading widget with visualization that assists in trading decisions. This year, we are moving towards migrating that framework for fixed income.

TD: Which analytical problems does TCA still need to address in fixed income?

WO: The problems stem from the need for more data to develop analytics. Fixed income lacks the volume and depth of prices that exists for equities. In BTCA, we are able to utilize our own peer data to assist in analytics plus help fuel our pre-trade cost models. In-trade and pre-trade decision tools are continually developed and enhanced, and are already well adopted in listed markets.

TD: How are impact costs measured?

WO: The costs are typically measured against the arrival price using far-touch as opposed to traditional mid-price for spread capture. Clients are also interested in comparing the costs in the peer group for a particular bond.

TD: What are the data points of most value for traders today?

WO: Beyond the price and size, the market looks at the type of security like high yield and investment grade as that determines the available data. Understanding the frequency of trading and market size is important but can bed challenging in fixed income. There are other points that can be helpful, such as whether a security is a new issue, as they are typically more liquid. Since benchmarks are not observable, composite and valuated pricing are used in combination with dealer quotes. This helps paint a more complete TCA picture.

TD: How do you bring that information together into a digestible picture?

WO: Within BTCA, we provide the flexibility of pulling the relevant data into one place. We also have an assessment called ‘order of difficulty’ where we ingest a number of data points, securities classifications, and the Bloomberg BVAL score to give insight into the amount of consistent pricing data as an indication of market liquidity.

TD: How is that presented?

WO: Depending on where the trader is in the workflow, we provide an easy way to view the relevant information. For instance, pre-trade, we integrate the analytics into our EMSs, while post-trade, we have interactive reports which aggregate data based on the view for the trader. Here we can combine all the aforementioned costs which include arrival price, the pre-trade cost model and peer groups. It also provides additional market colour on the bond.

TD: On an industry-wide basis, where do you see weaknesses in TCA today?

WO: The scope of data for fixed income has its limits as the trading frequency compared to equities tends to be relatively low. With fixed income, the limited level of information available in the market can present a general challenge when presenting TCA to clients. In those cases, data and analytics providers like Bloomberg can provide the necessary market intelligence.

It starts with finding the best available benchmark. For example, BVAL Bloomberg’s evaluated pricing service provides a wide breadth of coverage. In addition, our clients also have access to Bloomberg composite price (CBBT) price, which is based on indicative executable quotes in the market. In order to account for the variety of prices available, we give the clients the ability to create a waterfall based on the type of security, to pick an appropriate benchmark, reflecting the need to evaluate execution differently depending on the assets.

TD: What are the commercial pressures which justify Bloomberg’s clients investing in their TCA?

WO: There's constant pressure on asset managers to reduce costs. TCA has been a component of equity portfolio management and trading for over a decade. Now fixed income is becoming a bigger part of that conversation with the end investor. Having transparent and reliable TCA is something that asset managers want to present back to their clients.

Bound into that is the search for cost efficiencies. The industry-wide move towards more automated trading will ultimately improve costs on the asset management side. However, in order to improve automation it is necessary to build data around trade execution. TCA supports an assessment of which parts of the portfolio could move towards an automated platform.

TD: The biggest challenge for analytics in fixed income space last year and this year was quality of data; can you see that improving?

WO: Yes, but it’s a gradual process. One way we’re looking at this is the increase in automation we are seeing in fixed income that is relies on data. The use of Rule Builder, our automated trading tool has more than doubled between Q2 2021 to Q2 2022.

TD: How are you supporting clients on this journey?

WO: We are actively incorporating analytics into the trading workflow and are excited with the enhancement’s we’re looking to roll out in the future. We recently released our pre-trade cost model integrated into Rule Builder, which will allows a client to incorporate our cost model in addition the other data to determine whether to automate a trade. Furthermore, we are developing more analytics in the near future in support of RFQ optimization.

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