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Bridge at dusk with data on water

Where portfolio trading gains edge

October 2021
Michele Nicoletta

Vice President, Corporate Development, ICE Bonds

Using analytics to build highly tradable baskets of bonds

The explosive growth of passive investing in products such as Exchange Traded Funds or ETFs has supported the rise of new automated fixed income trading protocols. Now, the next advancement in efficiency is here.

The fixed income markets have long been known for their complexity and inefficiency due to the lack of automation. However, the market has seen advances in both data and technology in recent years, that have brought automation to segments of this asset class. In particular, the rise of portfolio trading - the ability to move large baskets of bonds quickly with precision - has been supported by investor demand for fixed income ETFs.

ETFs have a supply of shares that is flexible, meaning they can be “created” or “redeemed” to reflect changes in market demand. The associated rebalancing process has long been cumbersome, where constructing an optimal basket of bonds often meant relying on manual inputs such as spreadsheets.

While portfolio trading allows ETF issuers to automate their rebalancing process, it also affords asset managers execution choice as this advancement in electronic trading becomes more prevalent. Refining this type of trading has implications for overall transaction costs and execution quality. For those either new to the concept or looking to expand its adoption as part of their daily workflow, it is essential to have tools to assist in - or bring scale to - the portfolio trade construction process.

Now, the ICE Portfolio Trade Optimizer allows traders and portfolio managers to identify a basket of bonds that are a “best fit” for portfolio trading by scanning their portfolios and order lists against the composition of hundreds of ETFs.

Importantly, if a portfolio of bonds matches or is similar in kind to an ETF, there is a higher chance that it will be executed at a more aggressive price by dealers. That’s because matching the profile of a bond ETF helps to recycle those bonds into the primary ETF market.

By applying ICE’s benchmarking capabilities and risk factor analytics, the Portfolio Trade Optimizer allows users to assess the correlation between an optimized basket and an ETF composition.

To do this, the ICE Portfolio Trade Optimizer uses a matching engine to identify where particular securities show up in the ETF market. It then constructs portfolio trades that correlate with ETF portfolio compositions and identifies the highest-matching ETFs to a client list of orders and how well correlated the list is to ETF factors. This effectively provides a pre-trade modelling feature that looks at a portfolio and shows the ETFs that are most like it.

Users can adjust the basket until the intended investment profile is achieved. In this way, the tool brings more transparency to the portfolio trade construction process and aims to improve execution quality.

It is also possible to use workflow tools to isolate segments of the portfolio by security types, qualities, sectors, countries and currencies, as well as simulating portfolio performance based on specific interest rate or credit spread forecasts.

More broadly, ICE Portfolio Trade Optimizer helps to connect portfolio trading more closely to ETF primary market activity. By combining pre-trade analytics, ETF data and trading protocols on one platform, a once-complex task is increasingly being streamlined as the adoption of portfolio trading accelerates.