Data in Fixed Income Is a Good Problem to Have

6 Dec 2018

Data in Fixed Income is a Good Problem to Have

Data in Fixed Income Is a Good Problem to Have

 

Prior to the onset of the financial crisis and the subsequent wave of resulting global re-regulation, the majority of bonds and swaps trading activity within small-to-medium-sized asset management firms, hedge funds and wealth management firms was a game of dependencies. Specifically, the predominantly OTC-traded nature of the majority of daily trades and transactions in both instrument types meant that access to pricing and liquidity on an order-by-order basis for small- to -medium-sized asset managers largely depended on long-term relationships established with bank broker-dealers and non-bank brokers. At the time, scanning the overall global marketplace – or even the regional neighbourhood – to acquire pricing and liquidity data was, at best, a daunting and competitively prohibitive daily task.

 

Fast forward to 2018, and – arguably – the general bonds and swaps pricing and market data landscape for buyside firms of all ilk has changed dramatically. However, historically manual or quasi-manual desk-based trading processes and workflows related to price discovery and trade execution remain challenging habits to break. In EU fixed income markets, the defining characteristic difference between the bonds and swaps pricing and other market data picture in 2018 compared to the pre-crisis overlay is MiFID II, which significantly increased the amount of available data of pre- and post-trade data that was previously controlled largely by sellside broker-dealers. In the United States, the TRACE consolidated bonds tape began covering US Treasuries transactions in July 2017, in addition to the detailed corporate credit trade tape. Although data feeds associated with MiFID II do not currently match the completeness and granularity of the US bond markets consolidated TRACE, the European Securities and Markets Authority has outlined a clear path to the creation of a consolidated tape of European bonds transactions over the coming years.

 

In response to the new realities of a more level playing field for pricing and market data, buyside fixed income trading desks now have the opportunity to fundamentally re-think the manner in which they make use of and enhance trading operations using data. In this article, produced by GreySpark Partners on behalf of AxeTrading, analyst Willis Bruckermann examines how the challenges associated with the veritable torrent of bonds trading data now available on the market in greater detail.

 

Quantifying Exogenous Data Types

 

The vast majority of buyside fixed income trading desks in 2018 have the opportunity to re-orient historical trading practices so that they can become more data-centric. In doing so, those bonds and swaps trading desks would follow in the footsteps of buyside equities and FX trading desks, which undertook the shift to data-centric e-trading in the past by making use of a range of endogenous and exogenous data sources. However, the bonds and swaps market in 2018 makes use of a broader array of exogenous data types and matching methodologies than are present in other asset classes.

 

Unless and until buyside bonds and swaps trading desks are fully aware of and conversant in the various characteristics of the different actionable and non-actionable exogenous pricing data types for the instruments, attempts at changing their trading process and workflow operations to make use of the wide variety of matching methodologies and order types available to them will likely not be successful. Worse yet, if misunderstandings of the nature of different exogenous data types are propagated into a buyside firm’s OMS or EMS logic, attempts by the firm to create a data-centric fixed income trading desk may backfire, resulting in sub-standard execution outcomes.

 

Consequently, in 2018, a competitive buyside fixed income execution management system (FI-EMS) for fixed income trading must be able to handle and differentiate between four key categories of actionable, exogenous pricing data:

 

  • Bonds Axes (Indicative/Executable) – Although axes continue to originate primarily with sellside broker-dealers, buyside firms now increasingly disseminate their own IOIs or simply put buyside orders for execution on the market via the same tools used for sellside dissemination.

 

  • Firm Pricing (Executable) – Bonds and swaps brokerage trading venues are increasingly making use of firm pricing, which is pricing that is electronically executable at the shown price, such as central limit order book (CLOB) style venues, some of which are operated by exchanges.

 

  • Composite Pricing (Indicative) – Particularly important for benchmarking execution and TCA of liquid securities composite pricing sourced from one or more market data vendors can provide valuable insight into likely mid-point pricing levels for instruments given the current illiquid nature of the overall corporate credit marketplace. Conversely, some composite pricing methodologies can provide guidance on likely pricing for illiquid securities.

 

  • Pre-trade Dealer Pricing (Indicative) – Broker-dealers provide their buyside clients with pricing feeds that are not executable, but which provide further insight into likely mid-point pricing levels.

 

These four categories of actionable, exogenous bonds pricing information commonly available to buyside consumers in 2018 are further complicated by the range of brokerage platform and exchange or exchange-like trading venue matching methodologies, also known as execution methodologies, employed across the approximately 140 electronic fixed income trading venues in operation globally as tallied by GreySpark. In reviewing the characteristics of these venues, GreySpark identified the key methodologies that must be supported within the buyside FI-EMS to ensure that buyside bonds and swaps trading desks can access available liquidity in the market on a consistent basis. These are as follows:

 

  • Advertising Boards – Parties interested in trading a particular instrument, whether as liquidity providers or liquidity consumers, can advertise their interest in doing so in an indicative fashion, without obligation to trade if countervailing interest reveals itself.
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  • Auctions – Liquidity providers have the ability to bid on a security offered for sale or purchase within a specified time window. Auctions come in a wide variety of types depending on the objective of the brokerage platform or trading venue’s auction system. A range of common auction variants are used in the fixed income market, including price floors or ceilings on bids and disclosed or undisclosed volumes in the request for bids, among others.
     
  • Axes – Otherwise known as a market participants’ clear expression of interest in buying or selling specific, liquid instruments. Such axes may be kept confidential, but with increasing frequency are distributed to other market participants through networked solutions in order to enhance the likelihood of identifying a counterparty to trade with.
     
  • Central Limit Order Book (CLOB) Platforms – A trading mechanism for electronic orders that matches buyers and sellers of an instrument based on pre-determined rules. Orders sit in a queue until they are matched and executed on a priority sequence. The most common of these is price-time priority, but other platforms may use other priority sequences, such as price-size.
     
  • Chat-based Negotiation – Following the matching of countervailing interest, either manually or automatically, some venues place both sides into a private, electronic negotiating forum to negotiate the final parameters of a trade.
     
  • Multi-stage Price Formation – Some venues use price formation and counterparty matching processes composed of more than one trading stage. Such multi-stage price formation models are most common for illiquid or hard-to-price instruments where there is no clear market price. Multi-stage price formation usually combines a number of different trading models such as auctions, CLOB-like matching and work-up sessions, across segregated stages until a natural clearing price is found and all inventory is moved. Furthermore, such price formation may be utilised where the potential trading counterparties are not well positioned to quote prices.
     
  • Request for Market (RFM) – RFM refers to an RFQ or RFS that does not reveal whether the requester wishes to buy or sell, and so respondents reply with a two-sided quote that includes both a buy and sell price.
     
  • Request for Quote (RFQ) – Within this familiar market structure, venue price-takers request prices on an order of a specific size from the venue’s the price-maker(s). RFQ systems can vary based on the type of request recipients, and whether quotes are executable or indicative.
     
  • Request for Stream (RFS) – An advanced quoting system that allows buyside firms to request streams of prices, rather than a single price quote. Like RFQ systems, RFS may allow users to restrict (or expand) the price-makers to whom the request is sent and whether prices are executable or indicative.

 

Beyond supporting this broad range of matching methodologies, buyside bonds and swaps traders face the additional challenge of distinguishing the mix of firm and indicative actionable pricing. With the exception of all but the CLOB platforms and auctions – wherein pricing is always firm – as well as advertising boards and chat-based negotiation – which are always indicative – prices in any of the various methodologies can be either firm or indicative depending on the venue and counterparty (see Figure 1).

 

Consequently, a Fixed Income Execution Management System (FI-EMS) must evaluate and grade actionable pricing across all the aforementioned criteria as part of the trade execution selection process. Moreover, the execution choice following evaluation of market liquidity must reflect the buyside trading desk’s best execution policy, with full acknowledgment of the difference between firm and indicative actionable liquidity as well as the settlement risk associated with different counterparties. The latter evaluation again depends on the venue, as matching methodologies range from being fully disclosed, or lit, from the pre-trade stage onward through to fully anonymous dark trading, whereby the ultimate counterparty identity is not given up, even during post-trade settlement.

 

To further enhance the quality of a buyside FI-EMS’ liquidity evaluation and grading, a range of post-trade, non-actionable, exogenous pricing sources should be incorporated. These trade-tapes, including US TRACE, TRAX, EU MiFID II approved publication arrangements (APAs) as well as consolidated trade tapes must be ingested into the FI-EMS and understood as non-actionable pricing information that nonetheless supports pre-trade price formation, as well as best execution documentation; historical analysis of these data sources can inform trade execution selection going forward.

 

As in the case of actionable pricing feeds, non-actionable, exogenous ex-post facto trade tapes differ qualitatively, specifically in their scope and comprehensiveness. Consequently, the FI-EMS must not treat all post-trade data feeds uniformly; detailed logic for the handling of various feeds is required to contextualise the information contained within each non-actionable price feed, appropriately adjusted for the buyside trading desk’s objectives.

 

 

 

Figure 1: Table Explicating Firm vs Indicative Actionable Pricing

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Source: GreySpark analysis

 

Consuming Exogenous Data: Financial & Operational Challenges

 

Beyond the challenges presented by the diversity of data types that a buyside FI-EMS must service, buyside bonds trading desks find themselves with a range of financial and operational challenges in adopting data-centric trading policies and decision-making.

The greatest financial challenge faced by buyside fixed income trading desks in developing data-centric trading decision points is that the costs of the acquisition of the necessary data can be high. As of the end of 2018, the bonds market lacks a single centralised data feed or utility that aggregates and distributes global pricing and market data. Consequently, buyside trading desks must piece together the information needed from a variety of sources.

 

However, the significant cash-outlay costs related to individual data feeds, with pre-trade broker-dealer pricing sourced from third-party market data providers being the most expensive for many buyside trading desks, is not the only cost that must be shouldered by a buyside fixed income trading desk. For example, a trading desk that chooses to maintain its trading systems and data infrastructure in-house means that the variety of data sources and types results in higher cost implications from a labour-input perspective than may be the case for other, more normalised asset class data streams.

 

Indeed, the maintenance of a large number of data feeds and APIs across a range of different protocols and technical standards acts not only as a financial barrier, but also as an operational one. GreySpark understands that APIs for fixed income execution venues change frequently, imposing a high maintenance and servicing cost for the buyside firms receiving data feeds from them.

 

Furthermore, the large volumes of data from disparate feeds require consolidation and warehousing. While data management work can be undertaken in-house, in 2018 there are a range of specialist suppliers for these services that work with capital markets participants to maximise the utility of the data from feeds while minimising cost and effort for the trading firm itself.  Integration with such specialist suppliers is particularly advantageous for those asset managers that lack either the scale or the desire to undertake these tasks in-house (see Figure 2). AxeTrading, for example, complements and enhances their FI-EMS solution by partnering with leading firms and innovators in the areas of tick-level data warehousing as well as fixed income Big Data analysis that allows real-time insight from aggregated data feeds.

 

The evidencing of best execution for the purposes of MiFID II compliance adds yet another complication in handling various data feeds. Unlike pricing and market data feeds, which flow through the buyside FI-EMS continuously, compliance with the best execution evidencing requirements of various EU regulations means capturing time-specific data snapshots. These snapshots must then be stored securely, available for analysis on both a trade-by-trade basis and on an aggregated analysis basis.

Figure 2: Data Feed & Trading Tech Stack Diagram

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Making Use of a New Data Reality

 

Gaining access to the relevant exogenous pre- and post-trade data feeds is, unfortunately but unavoidably, in and of itself not sufficient for buyside fixed income trading desks to make use of the information contained therein: the feeds must be incorporated into systems that can undertake the appropriate analytic functions associated with buyside bond trading objectives. Consolidated bonds data feeds can present a particular challenge in this regard, as the vendors face restrictions on the downstream uses that they can permission due to restrictions placed on them by data contributors. Specifically, some bonds broker-dealers prohibit the onward routing of pricing data into third-party systems.

 

However, buyside firms are capable of finding workarounds to this problem or – more straightforwardly – of sourcing sellside broker-dealer pricing directly from single-dealer platforms (SDP) or multi-dealer venues. In 2018, roughly half of all sellside broker-dealers offer such data through their SDP’s API, while the vast majority of multi-dealer brokerage venues do the same (see Figure 3). Strategically combining these data feeds permits buyside fixed income execution desks to efficiently incorporate data from the full universe of liquidity sources within a FI-EMS.

Figure 3: SDPs & Multi-dealer Credit Venues Offering Data Feeds

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In the third and final article of this series, GreySpark will explore how buyside fixed income trading desks that choose to do so may avail themselves of technology that is moving from sophisticated sellside broker-dealer technology stacks to a broader audience of fixed income trading market participants, leading these desks toward a new source of alpha. This new source of alpha is the ability to leverage both the exogenous data feeds discussed above as well as endogenous data to derive a nuanced understanding of market dynamics to assist in improving trading execution outcomes in a range of ways.

 

Not only can execution outcomes be improved, but buyside desks con re-engineer workflows to be more efficient and efficacious, as will be detailed in the third article of this series. Once both exogenous and endogenous data feeds as well as the tools to maximise the benefits thereof are fully incorporated, buyside trading desks can develop continuous feedback loops that iteratively reinforce trading behaviours that lead to above-average trade outcomes while minimising those behaviours that diminish returns on investments due to poor execution.