In the fixed income market small decisions can have an outsized impact. A difference of just a couple basis points in a market that sees more than $4 trillion traded annually can mean millions or even tens of millions of dollars in cost.
The stakes involved mean there is a large incentive to effectively manage and structure the increasing amount of data that flows through companies operating in the debt markets. The lack of standardization creates massive headaches across recordkeeping systems, particularly in the context of reconciling cross-counterparty transactions. Without standardization there are data issues and with data issues, introducing robotic process automation or straight through processing of business functions is extremely difficult.
Yet, standards require choices, and difficulties can arise in the space between conception and implementation. Execution leads to questions like: How should the buyer/seller’s details be reflected? Or how should yield be calculated if there’s an embedded call in the security?
These are the types of questions that I face working for a company focused on introducing efficiencies into the debt capital markets. We aren’t alone. Bloomberg, a pioneer and behemoth when it comes to data normalization, has spent years working towards industry synchronization in the bond markets. Although these efforts are laudable, it’s hard for a company that makes money by creating and then licensing thousands of data points to be a completely neutral arbiter of sorting this information. Incentivizing universal acceptance is a tall order.
Standardization of data models has to be a process that works for all members of a community. Regulators are useful when it comes to setting standards, even if they can impose additional restrictions on individual companies. The European Banking Authority has been a leader in pushing for financial regulation that serves as a catalyst for market reform. The forthcoming Fundamental Review of the Trading Book (FRTB)—which is scheduled to be finalized later this year—will push banking institutions to adopt a comprehensive suite of rules developed by the Basel Committee on Banking Supervision to standardize the reporting of fixed income trade information.
Though the FRTB was finalized in 2016, the lengthy consultation period means that full implementation won’t occur until 2022. But to boil these changes down to their bare essence, a common set of requirements and mandated reforms give the banking industry a chance to make meaningful changes to how it creates and organizes fixed income trading information for corporate bonds. Right now there are no standards for underwriters or issuers to distribute information on new issuance; reference data may vary based on an institution’s choice of a reference data provider. The FRTB will help close this gap – and recent language from the SEC shows that the regulator is also examining how to address the problem.
The FRTB requires companies to create a consolidated quote system, which helps markets optimize balance sheet usage and improves institutional pricing data integrity for the fixed income markets. This has proven successful in a number of other markets, starting with over-the-counter equities in the States in the 1980s and expanding internationally to other parts of the market such as derivatives. Fixed income, because it continues to rely on voice and non-exchange forms of trading, has resisted the standardization efforts that have become common elsewhere. Aside from the incremental transparency provided by standardization, there is also a massive operational advantage to be gained. As mentioned, firms can only introduce robotic process automation or straight-through processing of core business functions once a data set has been homogenized. In an industry suffering from margin compression, automation can introduce cost savings that drop straight to the bottom line.
Pioneers are now forging a path beyond legacy infrastructure towards the digital future. For example, AllianceBernstein’s ALFA technology aggregates separate pockets of existing market data into a single user interface. This allows traders to filter bonds for specific parameters and make better decisions on trading. As a case study from the company points out, “central information feeds can result in better execution, lower transaction costs and faster investment of new cash flows.” Additional pre-trade transparency allows dealers to accurately price risk for institutional trading while hopefully increasing overall market liquidity.
If a standardized data model is introduced at origination, reference data and transactional reporting can be accurately maintained from primary offering through administrative activities, secondary market trading, audit and regulatory reporting and ultimately the maturation of these instruments.
Blockchain provides a unique technical solution. Deploying smart contracts can enforce protocol-driven administrative processing and automated recording of transactional events. While we are still in an early stage, it’s easy to imagine how a common ledger can help consolidate books and record systems and ensure that fixed income data sets are consistently maintained with permissioned access for the appropriate parties.
The trend towards transparency on both a pre- and post-trade basis and the resulting increase in data will continue due to regulation and the momentum behind electronic trading. This proliferation of trade data will allow for greater innovation in automated market-making activities and liquidity provision analytics. I’ve been involved in a few of these efforts with our sister company CBXmarket, where we are developing portfolio optimization tools that require the structuring of millions of data points.
The chairman of Blackstone Alternative Management, Tom Hill, recently described to IPE how asset management firms are entering an “arms race” in how they slice and dice data. Data storage and attribution can no longer be a manual, labor-intensive process delegated to the analysts. Implementing the appropriate technical solutions to provide the computational power to take advantage of the data opportunity will be crucial to find alpha. It is going to be a key indicator in how companies in all parts of the fixed income industry and the industry as a whole will perform in the decade to come.
John Mizzi is the chief strategy officer at Bond.One