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Request-for-quote (RFQ) remains the preferred tool for investors executing large fixed income ETF transactions globally, but the execution toolbox is beginning to expand. Modern technology is enabling more sophisticated, automated execution, making it increasingly worthwhile for investors to source liquidity efficiently as market practices evolve.
RFQs offer ease of immediate execution in a competitive arena. However, investors may want to consider selectivity in the RFQ distribution list to support best execution.
Sending a large order to a wide panel of liquidity providers that do not all competitively price the product can leak unnecessary information and potentially compromise execution. Price movement before execution is complete is also a risk, a dynamic discussed in industry coverage of pre-hedging practices. Some investors use systematic selectivity to mitigate this risk, which is viewed as positive for execution quality.
Working orders allow investors to instruct liquidity providers more directly and manually. A key advantage is the ability to spread a large transaction over a specified time window while giving the liquidity provider discretion to source liquidity in a way that minimizes impact.
This discretion can include providing internal liquidity or executing in the secondary market where appropriate. An execution benchmark can help manage investor expectations and gives the liquidity provider a target to execute against.
Investors are often benchmarked to a specific point in time, such as the closing ETF price. Executing at that point is intended to minimize slippage versus the benchmark, though closing auction liquidity varies widely by market.
In Europe, closing auction liquidity is typically thinner than in the US. Investors should also note that a liquidity provider will usually place a small portion of the order in the closing auction to achieve a fair price, with the remainder executed against the provider’s balance sheet.
NAV orders can be used to guarantee a given deviation from NAV. These orders can be sent via RFQ or provided directly to a liquidity provider.
As with other approaches, information leakage considerations apply. Because fixed income markets can be liquid, concerns about NAV moving adversely due to information leakage are particularly relevant for orders that are large relative to the underlying market.
Algorithmic execution of fixed income ETFs is evolving. The ability to process data from multiple sources and reach an automated execution decision in near real time is no longer theoretical, offering a new execution dimension beyond traditional methods.
Execution algorithms in the future could be designed to incorporate numerous data sources relevant to fixed income markets. They could continuously process data using artificial intelligence and execute orders according to investor preferences. In some use cases, algorithms could even send out RFQs to access liquidity. Early iterations of such products already exist, and development is progressing.
When using algorithmic execution, key questions arise around how much information is leaked to the market. The text emphasizes that understanding how algorithms access fragmented liquidity—especially in Europe—is essential to using these tools successfully.
Traditional execution methods, when used correctly, remain highly effective. However, technology is beginning to change execution options. Algorithmic execution is described as a continuous, data-driven, automated way to interact with fixed income ETF markets. While tools are still maturing, the direction of travel is clear: investors that engage early—building relationships, testing new technology, and developing analytics—may be best positioned for the future.
