Taxable fixed-income securities including bonds, treasuries, alternatives and other asset classes, creates both buy- and sell-side challenges. While representing only a smaller slice of the overall exchange-traded fund (ETF) market, approximately 21% of overall ETF assets under management (AUM), fixed-income ETFs are a rapidly growing area of interest for ETF issuers.1
These challenges stem from two characteristics of fixed-income instruments.
First, the fixed-income market data is less accessible compared to equities. Bond terms and conditions, credit quality and other essential data lack standardization, making the fixed-income market more opaque than it should be, even with the continued evolution of the Financial Industry Regulatory Authority’s (FINRA) Trade Reporting and Compliance Engine (TRACE).
Second, while request for quote (RFQ) platforms can make fixed-income securities inventory visible to the market, they are not necessarily optimized for the needs of the portfolio manager of a fixed-income ETF. Visible inventory is just the first step of the process. Portfolio managers (PMs) still need to analyze potential impacts on their portfolios against their benchmarks by analyzing data such as pricing, maturity, liquidity, credit quality, sector and country of origin.
So, even when fixed-income ETFs track a passive benchmark, it is not accurate to think of them as entirely passive as they do not hold all of the bonds in the index. PMs optimize the portfolio and negotiate with liquidity providers over the list of securities to be included in a specific basket before executing buy orders.
As a result, completing orders can be time-consuming and inefficient, slowing down the execution of orders and dampening the overall liquidity of the fixed-income market. It is not uncommon for negotiation between PMs on the buy side and market makers or authorized participants (APs) on the sell side to take as long as several days in the case of emerging market fixed income or very large negotiations.
However, this process can become much more efficient with an intelligent negotiation tool that sits between the seller and buyer that works alongside existing standard chat tools such as Instant Bloomberg. For example, imagine two scenarios.
An AP aggregates a portfolio of corporate bonds for a creation order and sends a chat message to their buy-side contacts at ETF sponsor firms with a list of bonds by security (e.g., using standard identifiers such as CUSIPs) and notional amounts.
A half-hour passes before the responsible PM reviews it, until the PM analyzes the securities in the basket. The buy-side team needs specific details on pricing, liquidity, credit quality, sector and issuer – amongt other fixed-income attributes – before placing an order. By the time they digest the data and complete the review, it is after market close.
The next trading day, the AP hears from the PM that some of the securities do not align with their investment strategies or remain within their benchmarks. The PM explains what needs to be changed, the AP rebuilds the basket and the loop repeats until both parties arrive at a mutually acceptable basket. The fact that pricing changes during the process increases latency and adds considerable risk.
There are two problems with scenario one: the process is fully manual and iterative, from order proposal to order close. Each step involves a hand-off that, in turn, triggers additional steps. A practical solution would connect portfolio construction directly with order flows so that front-office teams and market makers/APs can work through the process more efficiently.
To that end, BNY Mellon has built an ETF Intelligent Basket Builder inside ETF Center, a proprietary technology platform that helps monitor the entire lifecycle and operating model of ETFs. The product aims to provide a cutting-edge solution to the integration problem that impedes ETFs with corporate and other fixed-income assets such as treasuries and high-yield bonds.
Integration streamlines the process by allowing the front office to review a basket offered by market makers/APs in context. Data including quantity, sector codes (in multiple formats), maturity dates, liquidity scores, country of origin and pricing appear in line for each line item of the inventory. It allows for two-way negotiation, such as when the PM or issuer want to start the negotiation or assign an already negotiated offline basket to one or more market makers/APs. In either case, it bypasses the need for manual intervention, such as attaching a negotiated basket to an order.
To shorten the negotiation cycle, the PM can also use a feature that finds equivalent bonds, which provides confidence that attributes of an alternate security are similar, mitigating impact on the fund’s investment objective. For example, it might find a bond with the same credit quality and liquidity score in a different sector or country. Once an appropriate alternative is found, the PM adds it to the inventory. As a result, it could reduce the risk to the fund and eliminate the manual aspect of the negotiation process.
ETF sponsors could further benefit from built-in compliance and benchmark testing of each inventory component, such as 144A flags and automated highlighting of anything outside of sector or credit quality attributes. For example, if the inventory would push the ETF over the threshold of a specific sector constraint, the PM can recognize the issue in real-time without waiting for analysis in another application.
Finally, the benefits of applying automation to the process could accumulate over time. ETF issuers can manage all of their negotiations, easily monitoring counterparty activity with an accessible data set. This information could be a valuable tool for engagement between an issuer and the liquidity providers, creating better relationships over time.
There is simply less room for inefficiency in today’s market as conditions change. Taxable fixed income is a rapidly growing and important slice of the overall ETF universe. Reducing structural inefficiencies can prove to be highly beneficial – for the buy and sell side, for end investors, and for more efficiency and liquidity in the fixed income market.
1. $1.9B as of March 31st 2022, compared to $4.4B as of June 30, 2022. BNY Mellon Growth DynamicsSM