Constantinos Antoniades, global head of fixed income, Liquidnet
In the new landscape that has emerged as a result of the industry drive to make the corporate bond market more efficient, Liquidnet has developed a unique value proposition for its members.
First, it has become one of the largest pools of buy-side liquidity in the world in less than four years, with more than $20 billion in average daily liquidity (1). Second, it has achieved critical mass with 925 users from more than 360 firms since launch. Third, Liquidnet has helped address the larger size liquidity challenges by aggregating significant buy-side block liquidity, with an average trade size of $5.4 million in June. Last but not least, Liquidnet has worked to help its members save transaction costs, directly impacting performance; an independent study conducted by IHS Markit showed that executing a trade on Liquidnet with another buy-side firm saves on average 87% of transaction costs (2), when compared to the best price available in the IHS Markit dealer price feed.
How is MiFID II changing the corporate bond landscape?
Constantinos Antoniades: MiFID II places significant emphasis on best execution policy and process, as well as raising the relevant obligations on asset managers. Post MiFID II, asset managers need to demonstrate a process by which they can deliver the best outcome to their investors, with best execution being a dynamic policy subject to on-going validation and periodic adjustments.
When crafting and adjusting best execution policies asset managers must take into account, among other things, observed execution quality, access to electronic liquidity, changing market conditions, and changes in their business mix over time.
The combination of policy and process does not necessarily mean achieving the best price for every single trade, but it should constitute a framework by which the best outcome is consistently delivered to investors over time.
This becomes more interesting once you start seeing the new best execution framework through the prism of alpha generation, as opposed to merely a box-ticking exercise. We have already seen a number of asset managers that have crafted best execution policies that help them deliver additional alpha to their investors, which was an additional intention of the regulators.
The modern best execution policy can provide a better framework for better use of different venues for different types of orders, as well as different market conditions. This does not mean an auto-pilot process, but rather a process where the combination of more targeted use of protocols and venues as well as more data—together with trade acumen—can produce the best results for investors.
With a growing focus on best execution, what is the importance of TCA?
CA: In order to better assess the effectiveness and suitability of a best execution policy, you need to first be able to assess execution quality. Measuring execution quality requires being able to look at a data set that tells you, on a relative or absolute basis, which venues or counterparties have consistently delivered the best execution outcome.
This is where TCA plays a role and can shine a light on differences in execution quality. TCA is not meant to replace best execution, but it helps asset managers craft a better best execution policy by providing some of the data for the feedback loop required to evaluate the best execution policy and make trading decisions. In other words, it is a meaningful input into the best execution process.
Different orders may deserve a different execution strategy or destinations, and TCA helps asset managers quantify such differences and implement the appropriate execution strategy for any given order.
TCA comes in different forms, but most institutional investors are looking for something simple that can consistently provide them with an apples-to-apples comparison between venues and counterparties. TCA becomes particularly interesting when looking at results using the same or similar methodologies over a longer period of time.
Why has best execution become more science than art?
CA: There are two reasons behind this. First, there is now more data than ever before, which means you can make the process more data-driven and by default more “scientific”. Second, as part of the regulatory requirements, asset managers must be able to demonstrate and document actions to implement and evaluate their best execution policy. Consequently, this requires more data and a better audit trail, moving the dial away from the “art” end of the spectrum.
Is the new framework for best execution going to negatively impact those continuing with bilateral trading?
CA: Even though bilateral trading is still possible and can be done in several ways, whether a venue is a systematic internaliser or an OTF, MiFID II makes it a bit harder.
Given that the best execution policy is now going to be more rigid and will require more documentation as to how decisions are made, many trades that were previously executed bilaterally will likely go to an electronic venue or one of the newer protocols. This is the expected outcome in the post-MiFID II environment.
What tools are available to the trader, and what part does technology play?
CA: We have a very different market structure and ecosystem today than we had 10, or even five, years ago. The previous ecosystem was very simple, i.e. voice trading with some very small request for quote (RFQ) penetration for smaller orders.
In 2018 the ecosystem is far more complex and diverse, with a market structure that provides a lot of alternatives as to how you can execute different types of orders in varying market conditions.
Broadly speaking, we can split the market structure into three components. The first part is the traditional voice model whereby you call a bank, get prices, and trade. Eventually most of those trades will be processed through an electronic facility following MiFID II, but they are still effectively bilateral trades between the asset manager and a bank.
The second part is the traditional client-to-dealer protocols, like RFQ. Even though RFQ doesn’t largely create new liquidity, it makes the process more efficient, especially if you have a large amount of smaller orders. It doesn’t work for larger or less liquid orders given the risk from impact, but nevertheless it has a very important place in the market structure.
The third component is the combination of the newer dark, all-to-all, and buy-side-to-buy-side protocols. This is where we believe most of the new liquidity will be formed. The power of these new protocols is derived by their ability to eliminate barriers to trading and reduce liquidity fragmentation by bringing together buyers and sellers in an efficient manner. Historically it has been difficult, and often impossible, for these buyers and sellers to find each other seamlessly due to inherent market fragmentation and absence of technology to proactively seek and create liquidity.
Ten years ago, we had a single dimensional but heavily fragmented market structure with over 40 different banks and brokers each running their own venue. Today, the marketplace is more diverse, more resilient, and less fragmented — partially because electronic trading venues were able to centralise institutional liquidity and empower market participants to better find liquidity, buy-side and sell-side alike. Liquidnet’s growing pool of buy-side liquidity is a testament to that.
The new market structure also creates greater differentiation and alpha generation opportunities as different asset managers have access to different liquidity. The combination of new pools of electronic liquidity, more data, price aggregators, and better OMS and EMS capabilities create a lot more opportunities to find liquidity where it might not be that obvious.
As a result, theoretically, you could have two asset managers who have identical strategies with the same types of PMs and traders achieve a very different outcome for an order, depending on how they go about sourcing liquidity.
What is also interesting is that if you compare the fixed income market to the equities market, in equities the difference between the “haves” and “have nots” is legacy, cost, or very expensive data analytics. In fixed income, corporate bonds in particular, it’s more about behaviour and willingness to adopt technology. It’s not so much a question of who spends the most, but more about who makes the best use of the available solutions that ultimately wins.
Where do you see potential areas of innovation for execution quality for corporate bonds?
CA: When I started my career 24 years ago, there was no such thing as innovation in fixed income trading. You had one option: to pick up the phone. In the last four years we have seen more innovation in the corporate bond market than in the previous 20 years. At Liquidnet, we led the way with the creation of the first institutional dark pool, and more recently introduced our Virtual High Touch workflow; these are new ways to find institutional liquidity. The space is not static, however, and we are focusing in further leading the way with more automation, more targeted protocols, and analytics.
The innovations in the last four years were about the creation of new protocols and the proliferation of all-to-all trading and buy-side-to- buy-side trading. You can now target previous opposites to your order, find substitute liquidity, and rest larger orders in the dark without the risk of information leakage.
You can also use customisable workflows, such as Liquidnet’s Virtual High Touch, that intelligently guide orders through different execution protocols throughout the day based on the characteristics of the order. This is far from the limited options from five or 10 years ago.
Looking ahead, the new frontier in trading will be to better combine electronic liquidity, data and analytics, automation, and machine learning, together with trader acumen to achieve better results. This will accelerate the potential for differentiation, further alpha generation, and likely create a bigger gap between those who adapt to the new reality and embrace innovation, and those who don’t.
—
(1) As of Q2’18
(2) 12-month period Q1’17 – Q1’18