Two Birds, One Stone

Latest algorithms trade in two or more asset classes simultaneously

Kill two birds with one stone. That’s the pitch from some of the industry’s top suppliers of algorithms to those looking to trade more than one asset class simultaneously. In a relatively new development, a handful of major brokers have developed algorithms that let traders quickly lock in prices in order to hedge positions or conduct arbitrage trades across markets.

“We’re seeing a combination of client types showing interest in cross-asset strategies,” said Owain Self, who heads algorithmic trading at UBS for the Americas, as well as emerging markets and Europe. “Cross-asset strategies, and the advanced technology that makes them possible, facilitate efficiency and growth.”

UBS is one of several brokers, including Goldman Sachs and Credit Suisse, developing these algos. The ability to trade two different asset classes against one another in a single transaction, or strategy, using algorithms gained momentum last year as brokers either developed or released cross-asset algos.

The practice of trading a stock and a derivative at the same time is known as cross-asset trading. On the options side, in the U.S., trading cross-asset has been around for almost 140 years on an over-the-counter basis. On the futures side, portfolio managers have been able to manage their risks with index products since 1982.

The need to trade across markets has always been there, but the process has never been the most efficient. Traders want to get the job done as quickly as possible to lock in prices. Sometimes that happens. Sometimes it doesn’t.

In Times Past

Up until brokers developed these algos, a trader had to access each marketplace separately. And the buyside started doing this through sales traders.

The buyside would pick up the phone, give instructions and pay a fee, according to Andy Nybo, principal and head of derivatives, TABB Group. The sales trader, in turn, would contact the cash equities desk, the futures desk, the options desk or the foreign exchange desk-whichever desks were handling the legs of the trade. But it can be more than three times as expensive to trade a futures contract over the phone than to trade it electronically.

Cross-asset trading via direct-market-access platforms started to take root earlier in the decade. This sped up the execution process for the buyside.

But some say DMA can be cumbersome. It can be awkward trying to lock in both legs of the trades.

Recent advances in electronic trading have taken much of the legwork out of more complex trades. The technology to trade more than one asset class on a single platform has evolved and made it easier for traders to develop more advanced cross-asset strategies, according to Harrell Smith, head of EMS maker Portware’s product strategy group.

Algorithms themselves are well established. And the benefits of algorithmic trading this decade have been faster and cheaper trades.

Linking Markets

But most algorithms have generally been asset-specific. Their functionality has been isolated within individual markets: equities, futures and options. New cross-asset algorithms have started to bring these markets together.

This has been helpful to buyside traders looking for hedging and arbitrage opportunities in a floundering equities market.

Jeromee Johnson, vice president for market development at BATS Trading, told Traders Magazine: “Cross-asset algorithms are certainly an area of focus right now, and are becoming more broadly available. Specifically, tying equities to options is where you see most of the work being done. You are also seeing cross-asset algorithms and related technology-auto-hedging, for example-being tied between equities and FX.”

Unlike DMA, the algo does the work for the trader, slicing the order up and venturing out into the equities market and the options market simultaneously-or the futures market, or the ETF market.

But the advantages to these cross-asset algos aren’t limited to hedging. They facilitate arbitrage trades, as well. They can help traders quickly take advantage of small discrepancies in price between an ETF and its underlying stocks.

Sophisticated hedge funds eyed the benefits cross-asset algos promised and embraced them from the outset to hedge and make some money through arb strategies. Some traditional, long-only asset managers have also climbed aboard.

Industry pros see the technology as the next frontier for algorithms. But so far it’s made slow inroads among most hedge funds and traditional asset managers. That should change, experts say.

Trading across asset classes is important to the growing number on the buyside who use derivatives to measure and manage their risk. As more join those ranks, they will want tools that make the process more cost- and time-friendly.

Dipping a Toe

Credit Suisse, Goldman Sachs and UBS have each said that the user base for cross-asset algos has expanded to encompass the buyside spectrum, including traditional asset managers.

For example, as these traders establish a position in a foreign equity, they may want to have the same algorithm trade the foreign exchange fill-for-fill simultaneously to reduce the trade’s potential FX exposure, said Pankil Patel, director of the U.S. trading desk for Credit Suisse’s Advanced Execution Services. Or, if a trader at a traditional asset management firm wants some portfolio insurance on a sizable position he has in a large-cap name, a cross-asset algo would make it simpler for him to sell some covered calls against it.

The technology makes it easier to get into different marketplaces. “Portfolio managers are definitely driven to find returns,” Patel said. “That’s the main reason why people are going into these different markets. It used to be extremely costly.”

Cross-asset algorithms are a logical extension of where algo technology and the industry are headed, according to Sang Lee, a managing partner at Aite Group whose research focuses on electronic trading across asset classes and market structure issues. But their adoption rate so far is relatively low compared with algos used for equities.

The sellside needs to educate their clients more on how to use these algos, he added.

“There’s still a lot of education going on from the brokers’ perspective,” Lee said.

Some buyside traders who don’t use cross-asset algorithms said they still find

the concept a positive development. One hedge fund trader who requested anonymity said he trades stocks and options together using DMA.

He said he’s intrigued by the possibility of using an algorithm to trade them simultaneously.

“But you have to make some quantitative assumptions along the way,” the hedge fund trader said. “That’s the trick. I’d be asking [brokers] questions about who makes the assumptions on the value of the options and how that works.”

“They’re Phenomenal”

New Jersey-based hedge fund Fairfield Advisors has used cross-asset algorithms since they first hit the Street. Fairfield trades in equities, commodities, commodity options and equities options, said Jeff Benton, who co-manages it. And the hedge fund sometimes uses Credit Suisse’s cross-asset algos to establish positions according to its multiple-asset models.

“The fact that you can now go to options and stocks simultaneously is very helpful,” he said. “[The cross-asset algorithms] are a phenomenal technology.”

Why? For starters, they can mitigate risk. When a trade involves multiple legs and multiple platforms, the risk of slippage certainly increases dramatically, Benton said. But being able to execute two asset classes simultaneously removes that execution risk from the trade, he added.

“You might have a limit where you need everything to happen to get the return you’re looking for,” Benton said. “Obviously, being able to lock in the trade-knowing that you can get all of the various legs of the trade done at once-makes your life significantly simpler.”

Get the Full Picture

Cross-asset algos are frequently used for hedging and unwinding. But they’re also used in pairs strategies or to help traders acquire or sell off positions that a trader believes are positively correlated, according to UBS’s Self.

And the algos ultimately help the buyside save money, added TABB’s Nybo.

“[Buyside] commission costs go down,” Nybo said.

“Their activities become more efficient. Their risk management processes are more effective. Their exposures can be managed at a much more granular level, in a more real-time fashion. You can buy a block of stock and try to hedge it in the options market, or in the futures market, and if the market moves, you have missed opportunity and additional costs if you can’t get it off at the right price,” he said.

For brokers, building a cross-asset trading infrastructure is no walk in the park. Most firms have structured their systems and strategies along single asset classes.

This has meant that each desk would only see its specific leg of a cross-asset trade, said Aite’s Lee. And while a limited perspective hasn’t necessarily been a bad thing, it has meant that neither side would have a full picture of a trade.

Brokers also must ensure that their equities, futures and options infrastructures are well integrated, industry experts said, and not calcified into segregated silos. Also, enabling simultaneous access for algos across asset classes to appropriate venues presents large challenges for brokerages, he added.

“You need to have connectivity to all the execution venues,” Lee said. “And you need to have connectivity that incorporates the unique functions that each execution venue exhibits-things like special order types.”

Into the Futures … Market

Firms also need enormous scale to do cross-asset trading, he said, particularly for a program order that sprays, say, 200 names across 10 venues.

UBS will unveil two types of algorithms-one for single-stock trades and another for portfolio trades. Both types are expected to be released in the third quarter.

UBS can build cross-asset algos because it is electronically plugged in to the necessary markets-the cash markets’ displayed and non-displayed venues, as well as the futures, options and ETF markets, Owain Self said. A cross-asset strategy will use UBS’s algorithmic trading engine to send high-speed orders to the best venues-trading both asset-type legs.

A common scenario for using UBS’s algorithm would be a pairs strategy, with a single-stock order on one leg and an index futures contract on the other. The cross-asset algorithm can pair a single cash equity with anything UBS’s execution platform can trade, including an ETF, a single-stock futures contract or an options contract. 

The objective and capabilities on the program side are the same as those on the single-stock, Self said. The algo helps users, for example, who want to liquidate a hedged portfolio remove the hedge in the same ratio, he added.

In such a strategy, the algo ensures users aren’t restricted to using a very schedule-based algo, such as VWAP, but can instead use any single-share algo, Self said.

“As soon as the executions are happening, the algo starts working both sides in sync,” he said. “So it’s taking the hedge off in relation to the cash component.”

The next stage for UBS algos, according to Self, is to offer the same functionality when trading multiple futures contracts.

Goldman Sachs introduced enhancements to its options algorithms recently that let its customers auto-hedge their positions in the options market. This allows options traders to simultaneously execute an equity hedge in real time as an options order is filled.

Goldman Sachs designed the enhancement for its seven options algorithms, said J.P. Xenakis, head of electronic listed options sales for Goldman Sachs Electronic Trading. Once parameters are set for the options leg of the trade-such as the degree to the hedge, what kind of delta should be hedged on each trade and aggression level-the enhancement launches one of Goldman’s many equities algorithms immediately.

“It’s meant for clients who want to put on a stock hedge immediately as they’re executing options,” Xenakis said. “So, we give the client some choices on how they’re going to pull this hedge off.”

Last year, Credit Suisse began offering clients what it calls “transformational algorithms” to trade equities, futures, options and foreign exchange simultaneously.

Seeking Exposures

One of the firm’s cross-asset algos, Liquidity Transformer, changes liquidity from one asset class into another.

All orders for the cross-asset algos run through the AES desk. After a customer sends an order for exposure in, say, an ETF, the algo kicks into gear.

A smart order router-which sits under the algorithm-starts searching in real time for information from any possible source for exposure. An ETF is a representation of some underlying assets, and is made up of those underlying assets’ values. These values could include the underlying stocks, effective futures price for those underlying stocks, other ETFs leveraged to that particular ETF and so on.

The router accumulates equivalent exposures to the desired ETF. It then converts all of those values back to an equivalent ETF price. In the end, Patel said, Credit Suisse will have sourced the liquidity and found the equivalent products within the different asset classes.

All of the fills that the customer gets back are in equivalent terms of the desired ETF. Credit Suisse trades all the assets and then swaps them into an equivalent price.

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