Markit: Building a $13 billion dollar company

How we took data and sold it eight different ways for success

Yesterday, the company I was on the founding team of — Markit — announced a merger with IHS, forming what is now a $13bn company.

Reading some of the press on the merger, I saw a quote from Lance Uggla, my old boss, the CEO and Founder of Markit.

“Every time you have a piece of content, you can make money in different ways. And what’s more important is that our customers demand this content”

That’s exactly the brilliance of what Markit did. We took data — sold it. Then we added key complimentary datasets. Then we found ways to sell it again.

What exactly do we mean here — and how did we make this happen?

1. At the beginning…

Today, Markit operates in many different markets. Credit Derivatives though is what the company was built on in the early days.

In the early 2000’s, the credit derivative market was pretty small. The product was established, but it was dominated by one bank: JP Morgan (indeed — they had created the product). More importantly it’s potential was being held back by the lack of independent data.

Unlike stocks, credit derivatives don’t trade on exchanges — they were traded over the phone! That meant there was no-one in the middle reporting what the price of all the transactions was like with NYSE. The demand was there but the market infrastructure wasn’t.

Without this data it was difficult to verify how much your portfolio was worth after you had traded. This caused problems for the banks and the hedge funds who were eager to get into the market.

Lance and the team had felt this pain acutely at TD Securities, who provided the initial seed funding for the business. They also knew the potential of the market. They decided to fix it.

Hence, we started building a pricing database. We took data from all the banks, cleaned it (a lot of the data was very poor quality) and then created daily snapshots of the value of each derivative. Our first product was born.

2. The next product….

As we did this, we had noticed another problem — poor identification data on the trades. With Ford stock it’s very clear what you are buying. But to trade the credit derivative there was a problem. Ford has tons of companies all issuing debt, and if you don’t trade the right company your derivative is worthless. Indeed there had been lawsuits over this very issue!

As we were collecting all the pricing data, we observed how all the banks on Wall Street had inconsistencies on their reference data. Another problem to solve — great!

We weren’t of course the only people to realise it. Of course the banks themselves had. Three of the main players had been working on what was termed Project RED at the time (RED for reference entity database). However, they had trouble convincing the rest of the banks to use it. They didn’t want to use something operated by the three dominant players — it would make them reliant on their competitors. Banks hate that.

So they put RED up for tender. We were still a tiny little firm at the time, based in a converted farm barn next to lush green fields in St Albans, north of London. Our neighbours were sheep. We had 10 people in the firm then, and we knew the likes of Moody’s and Standard & Poors were competing to win this. What chance did we have?

I still remember the night before our bid was submitted. The whole management team was frantic writing and re-writing the bid submission. We knew that our (still very young) pricing data gave us that chance. The pricing data and reference data naturally fit together. We would be able to tell you what it is, and what it’s worth. Much better and more efficient to have them both from the same source vs piecing it together from different sources. It’s like Tesco gives you the price of the item, but you need to get the barcode and description from Sainsbury’s.

A problem, but also an opportunity….

One of the banks selling the database was JP Morgan. That presented an opportunity, but also a big problem.

They were so dominant they had a massive price advantage against everyone else when trading the product….but we were bringing transparency! That was only going to do one thing — reduce their advantage (and indeed it did in the end). That was the problem.

The other two banks though — Goldman Sachs and Deutsche Bank — were keen on us and were clients of ours at the time, and we knew if we won their support we could get JP Morgan to consider investing in us. That was the opportunity.

We won the bid. Approximately nine months later the negotiations were concluded and we had our prize. Not only had we managed to win the business — but in the process we managed to get all three banks on board as investors. Why exactly they invested is a post for another day….

Apart from a quiet celebratory lunch at a country pub near St Albans (the feeling that day indeed was that our future had been secured), we got right to work on it.

3. Indices

At the same time, we had been thinking about two other areas which had grabbed our attention. The first was the business I would go on to run in North America — indices. These are like the FTSE 100 or S&P 500, but for credit derivatives.

There had been various aborted attempts by the industry to create indices for people to trade. In 2004 they all finally knocked heads together and created CDX for North America and iTraxx for Europe. Everyone had been waiting for this — it was clear this was going to drive expansion of the market. And how!

Before the indices — the amount of credit derivatives traded were counted in the millions. After — they were in the billions!

We knew we had to be involved. We had the pricing data and reference data — we knew we could play a pivotal role. Lance however was audacious.….he wanted to own them!

We suggested this from the start. It was too early, but the seed had been planted. We settled for the time being as the company that ran the indices.

At the time — a prominent member of the industry told us — ‘You are just a poxy little data company — what do you think gives you the right to own the biggest thing in the market!?’ Indeed we were just a poxy little data company — but we had grand ambitions. In fact, this prominent industry member would go on to join us years later.

So we got the contract to run the indices. I was tasked with managing them in New York. Every 6 months we would re-balance them — it was the most intense and satisfying weeks of the year for me.

I still remember the very first rebalancing and being in the middle of it. It was one of the most important weeks of the year for us as our work was extremely visible to the whole market. I got a call from London where I had just moved from 5 months ago. It was my brother — my mother had had a stroke. I didn’t know what to do. I went into the office of Kevin Gould — who was also a co-founder and built all of our North American operations. He said immediately ‘you have to go home’. I’m still grateful to the day for not even having to ask. This exemplified the empathy — and teamwork with which the business was run. A quick thank you to Mi Ran Park and Kiet Tran (in his first week on the job!!) for picking up the pieces in my absence.

Unfortunately, my mum passed away before I managed to get home. The way the company treated me at the time though helped me get through it no end. I hope she is looking down happy.

Anyhow — the point is that the reference and pricing data got us the role for the indices. Momentum was building.

4. Turning data into a valuation

At the same time in London, we had started working on building out a valuations business. Many of the smaller firms in the market didn’t have the operational capacity to value their portfolios quickly and meet their reporting deadlines — even when we gave them the data. So we built the tools to do it for them. With the indices being so big — this was a part of the solution.

So — now we had the pricing data, the reference data and the index data — and with all that we could now provide the valuations data! This is a huge business for Markit today.

5. Portfolio Management

With all this data we turned out eye to another problem — post-trade reconciliations. Every day, participants in the market needed to agree with each other what the trades were between them. Because of the arcane approach the market had taken to start — exchanging faxes to confirm trades — this was a big problem.

Also very important was margin. Once they agreed what trades they had against each other, they had to calculate how much the market had moved and affected their positions.

A quick note on derivatives to give background here. When two traders agreed a derivative trade, basically they have agreed a long-term contract — the value of which is dependent on something else. At the maturity of the derivative contract — they look at what the value of this ‘something else’ is and agree to make a settlement payment depending on this value and and in what direction it has moved.

A simple example could be — carrots are 20p each today. In five years, if they go up 6p I will pay you 6p. If they go down 4p, you pay me 4p.

The standard credit derivative trade was a 5 year trade. At the end of 5 years one side would make a payment to the other. But there was a risk here. What if you made a trade with someone, they owed you at the end but went bankrupt before maturity? You are out of pocket!

To counteract this, the concept of variation margin was created. Every day — you would agree what the value of the derivative contract is, and whoever owed the other would place assets or cash with the other party equivalent to how much they owed them.

The rules around this margin was a key contributor and mitigator for the financial crisis of 2008. It was a key mitigator because almost everyone had margin with everyone else and it helped to limit potential default amounts between the banks.

Almost everyone I say though. AIG, due to it’s AAA credit rating at the time didn’t have to give variation margin! And they ended up owing more than almost anyone!! It’s because they owed so much, and the banks would have been in so much trouble if they hadn’t received these sums — that they were bailed out, and Lehman Brothers wasn’t. But that’s a story that’s now been told.

Back to the point. To calculate the variation margin, you needed the pricing data, the reference data and the tools to run the valuation. Guess what? We were doing all three of these!!

5. Legal Documents

What next? Next was fixing the process of legally agreeing the trades after they were agreed. Typically, the traders would agree the price and then hand over to their back office to finalise the legal documents (known as confirmations). In the early days of the market, this was done by faxing signed documents back and forth.

This was fine when hardly any trades happened. But trading exploded — especially after the indices were introduced. The fax machines didn’t cut it anymore. Indeed, some banks were burning through the whole life of a fax machine in weeks!

A technology solution was required. Initially, it was delivered by a company called DTCC. They built it and operated it. But they were effectively a non-profit utility — and that meant they were slow…..

The industry having seen how fast we executed — supported us to take over the joint operation of the electronic confirmation business. We spun off part of our business and merged it with theirs.

Again — we were finding new ways to monetise data we already had. The reference data again came to the fore here. We also knew — with the pricing data and valuation tools we could make this product very powerful indeed.

6. Auctions after companies went bust…

Back in New York, we had been busy spotting other new opportunities. In 2005, the first default occurred since the launch of the new indices — Collins and Aikman.

What is a default? In credit derivatives you are effectively buying insurance on a company’s debt. If it defaults, you get paid.

Again our services were deep in the spotlight. People wanted to verify they had the right reference data. That was us. People wanted to understand how the product was trading after default (it continued to do so for a short while still) so again they needed us. Finally, we managed the indices, so we were at the heart of the conversation again because Collins was listed in the index. This conversation was about solving a new problem caused by the increase in trading since the indices launched — how to settle the defaults.

The original mechanism for settlement was no longer fit for purpose for various reasons, and a new solution needed to be found. And again, something we had been working on on the side came to fruition — fixings.

Fixings was a method we had developed with a company called Creditex (who we partnered with for this business) to establish the value of any asset beyond doubt. The methodology involved running an auction which eliminated any dubious submissions (and penalised them) and came to a transparent trusted price. We proposed it as the solution here, and Wall Street quickly agreed after reviewing it.

We now had another new business which I ran — Credit Event Auctions. We had no idea how much in the eye of the storm that was coming in 2008 it was going to be. Across 2008 and 2009 we operated many auctions, including for Lehman Brothers and Fannie Mae/Freddie Mac — the US mortgage giants.

Our pricing data, reference data, indices work and fixings sideline now had us at the heart again of the next big initiative in the market.

We finally get the indices…

In 2007 — we finally bought the indices we had sought back in 2004 — and they provided the seeds for a totally new business line in the company.

7. Trade Netting

In 2008, again we found another way to use our data — portfolio compression. At this point, the volumes for credit derivatives were reaching the tens of trillions. Worse, there was a lot of crossover trades across the market which added to the administrative burden.

Imagine party A buys 4 oranges from party B, party B buys 3 from party C at a later date and C buys 8 from party A. Instead of agreeing to just hold the net amount of oranges they had — they would have legal agreements for each separate trade they had done — and keep it that way. This is how the credit derivative market operated at the time.

Netting things down wasn’t easy but it was doable. And we won the mandate to do this, again partly because we had the reference and pricing data!!

8. Trade Reporting

The last major trick we pulled with the data in credit derivatives was after the financial crisis — trade reporting. After the crash, the regulators were hungry for more data. We were already providing them a ton of useful insight, but they needed more. So they asked for proposals to build trade reporting platforms. In the credit derivatives space — yet again we won.

If you got this far….congratulations! Almost there….

This is a story of how we began with one dataset, added a few others and synergistically leveraged them into creating totally new businesses within the firm. Today — we have expanded so far beyond all of the above by following the same principles in other asset classes and markets.

Business is never this simple — but in my mind if there is one core principle that drove our success above many others — it was this — we never stopped looking for new ways to add value to our clients.

We won the reference entity business partly because we had been pro-active in spotting problems in the portfolios our customers sent us. We won the indices business because we worked on figuring out how what we already had could be part of the process.

Not everything we did in the early days had obvious monetary value initially. A lot of it — such as the fixings — were things we did simply because it put us closer to the heart of the market we were serving.

This principle of adding value is one I will never forget — and will continue to practice in my next ventures. You don’t just do it. You ask yourself the question every day. What more can we do? How does it position us for what is coming?

Gratitude

For me, Markit has been an amazing journey and I’m forever grateful to Lance Uggla, Kevin Gould, Tom McNerney (who between them taught me so much) and all the other colleagues I was so lucky to have worked with in my decade there. I’m honoured to have been part of it.