Clarus Financial Technology

What’s the story behind Tradeweb block trading?

Tod has covered the background to this topic before when he looked at block trading in a blog last year – “CFTC Block and Cap Sizes”. I’ve done some googling, and I’m pretty certain we are yet to transfer to the “post-initial” phase, but I sure hope Tod has taken the garbage out by now….

Stuck as we are with the same cap and block size thresholds since trade reporting began, I thought I’d have a look at the data available and see if there’s been any changes. And do it quickly, in case the CFTC should, ahem, maybe think about re-running its’ analysis….possibly via an easy-to-interrogate, curated data service like Clarus provide?

D2C and D2D Dichotomy

One thing is for sure – there seems to be a distinct difference between the handling of Block trades between the D2C and D2D SEF platforms. I’m assuming this is due to the fact that the D2D SEFs always have the ability to transact a trade off-SEF. But it’s worth noting that Tod mentioned there is indeed confusion over the rule:

“Block Size: The notional threshold for which a trade can be a “Block” and hence must be executed away from a SEF. (Disputing “must be executed away” and “can be executed away” is the subject of another article).”

As a potential result of this confusion, we know the block amounts transacted on-SEF at the D2C SEFs (Tradeweb and Bloomberg, available via SEFView). But D2D SEFs do not strip out what is block-related notional and what is transacted “normally”. We’ll revisit this at the end of the post.

The Data

We’ll use the new CustomView within SDR Res to look at the first cut of the data. This shows how much block trading activity we’ve seen since the beginning of 2014 on a month-by-month basis:

Showing:

The custom view also allows us to strip out Compression and List activity. We know that this form of execution has been a feature in our markets over the past 2 years, and may not necessarily reflect where true liquidity lies. Stripping out these volumes and looking at the percentages traded On- or Off-SEF is interesting:

Showing:

Huh! So, in answer to Tod’s confusion, I guess the market has voted with its’ feet and decided that Block trades are fine to execute on-SEF!

Customer vs Dealer SEFs

There’s something else in the data that makes for fascinating geekery as well though. As we know, D2C platforms have shown stellar performance during 2015, accounting for almost 100% of on-SEF volume growth in Rates space this year.

And these SEFs are the only ones (that I can see) who separate out Block volumes from non-Block on their daily reporting. And that gives us yet another time-series to interrogate!

So what percentage of D2C business is Block trading?

We know that D2C now make up over 55% of all trading volumes in USD swaps, even when excluding compression-related activity. So how much of this is related to block trading? That question actually largely depends on whether we are looking at Tradeweb or Bloomberg:

From that chart, we can see that:

Overall, it’s interesting that the best performing platforms this year are also the best places to go and trade a block if you want to do it on-SEF.

And if you have a block to trade on-SEF….

….the market is choosing Tradeweb. We know that Bloomberg are seeing some impressive market-share growth overall, but in this particular area of the market, Tradeweb really does rule. They now see a huge 75% of Dealer-to-Customer block trades go through on their platform:


It’s worth mentioning that this 75% figure is so high that my initial assumption was that Tradeweb have some kind of  “block trade reporting” tool for trades arranged off-facility – maybe akin to reporting an out-of-order book block or EFP in futures space. But we checked with them, and no such tool exists. In which case, it’s a mightily impressive market share!

Are we therefore seeing a D2C market place where Bloomberg is used for small trades, and Tradeweb is where you go for execution of good size? I’ll have to revisit that question in the data another time….

In Summary

Given the overall picture, it’s an interesting side-story within the data, which can be summarised as:

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