The evolution of pricing bonds and the data journey

Jason Waight, head of regulatory affairs, Europe at MarketAxess, considers why access to flexible data is key to using new trading protocols in fixed income

Jason Waight

Buy-side traders in the fixed income markets can have very different execution goals from one trade to the next. Price may be the key target for one investment, speed for another. The size of orders can also vary considerably, which impacts trading costs. 

Help is at hand via new trading protocols and a wider variety of trading counterparties that allow firms to price bond trades in a variety of ways, according to circumstances.

All-to-all trading is not new conceptually, but is still being adopted by many firms. It is allowing non-bank traders to become price-makers, either to cut costs or even generate alpha, while still engaging in a pool of liquidity containing traditional dealers. 

Internal crossing is also a valuable tool for European investors, as asset managers can find portfolios buying or selling the same assets simultaneously and exchange them at a mid-price instead of paying the spread in the market. US markets are expected to allow this model very soon, following a proposal made to the US Securities and Exchange Commission in June. 

Dealers have built auto-quoting algorithms to stream prices to their clients, which can allow for rapid trading, typically in smaller-sized clips.

These different trading styles allow investment traders to find the right counterparty and trade size, at the right price. To be confident of the pricing being offered – particularly where new activity extends beyond existing skill sets that trading desks had historically needed – they have to be well supported by data and analytics to make the best decisions. 

 

Understanding the price  

While bond prices are typically less volatile than equity markets, pinpointing the right price is challenging. The frequency of trading for any one instrument is far lower in bonds than in equity, creating gaps in pricing data. The number of individual instruments in bond markets is far higher and trading more fragmented as the market trades bilaterally or over the counter. These are time-limited instruments, making the proximity to issue or redemption a consideration. That makes the process of price formation more time-consuming and complex.

There are also market structural issues that make pricing less consistent. Prices for the same bond will vary depending on whether a trade is in the interdealer market or the dealer-to-client market, and the pricing of bonds for a smaller $300,000 trade will also be quite different to the pricing of a trade for a $10 million block. Consequently, looking at bare numbers alone is not a good guide to pricing a trade in the future – they need context.

Additionally, in 2020, price volatility for some parts of the credit space has been closer to the volatility that might be expected in equity markets, as investors look for alpha in a near-zero rate environment. 

This volatility is increasing the appetite for new trading protocols but, to take advantage of them either as a price-maker or a price-taker, firms need to use pricing information that is timely, relevant and accessible. This demands sources of pricing information that capture multiple inputs and use them appropriately. 

When asset managers engage with counterparties via new trading protocols, they need third-party data to benchmark the prices being offered. They can use proprietary data as well, but it is constrained because it naturally has biases based on a firm’s own activity rather than taking in a market-wide view of pricing. Counterparties will see a broader market of trading firms and activity and will stand their trading decisions in that context. 

Where third-party pricing feeds are used, they must be transparent so they can be correctly understood and represented within analytics. The data used for one style of trading may not be appropriate for another. 

For example, in all-to-all trading, firms can support price-making if they can be confident of where a bond ought to trade at a given point in time, making the timeliness of data vital, particularly if they are seeking to take advantage of volatility. That can deliver returns directly to investors. 

Internal crossing removes the need for an external trading mechanism, but it must be supported by a composite price that clients can see and trust, to ensure both buyer and seller received a fair deal when the instruments were exchanged. 

 

Building the picture 

Having the right data sources is key to employing varied and effective trading protocols in the fixed income space. No single dataset will be sufficient given the different priorities and outcomes each is seeking to achieve. Access to data should reach across functions, so anyone within trading, portfolio management or risk who is needed to support a decision can do so in a timely fashion. MarketAxess’ Axess All platform – the closest it has to an intraday tape in Europe – has seen a growth in use beyond the trading desk to support a single view of the market and better management of trading as a part of the larger investment picture.

Data must also be accessible to support post-trade activity for each of these trading models. Whether using transaction cost analysis or other execution quality analysis, traders will need to benchmark execution against several measures, including the prices offered across several possible execution choices, and this hinges on the right data being available. 

Improving execution quality over time is only viable if traders can use independent data to check the prices they are provided, and to frame that within similar market activity at that point. That information should include information that will help to contextualise implicit costs. 

These costs might include that of not filling an order or of the market impact that a trade incurs, as well as explicit costs such as the fees charged by a trading platform and the spread at that point in time. Through quantitative analysis of execution quality across different trading protocols, buy-side firms have been able to exert pressure on their sell-side liquidity providers to provide better quotes. 

During the height of market volatility in March, buy-side participation in all-to-all trading on MarketAxess’ Open Trading protocol nearly doubled. Firms that have begun to use this and other protocols for the first time are becoming more flexible in managing market risk and returns. Flexible access to data must develop in parallel to support this evolution.

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