Trading & Risk Technology

LEIs are like a party for counterparties, albeit a festivity in which the guests are to arrive at different geographies and different times. The Legal Entity Identifiers (LEIs) are more than digital fingerprints that firms can use to identify counterparties; they represent a time- and money-saving infrastructure whose hour is at hand — or will be in a few months.

Setting the Timetable

“The ability to access data not only on counterparties but also on counterparties’ counterparties will be invaluable for banks and their trading clients in settling risk limits and monitoring them,” Keith Mullin of capital markets weekly International Financing Review wrote in TabbFORUM this week. Mullin makes his case for mandatory LEIs, suggesting regulators offer a transition timetable to those who would need to adapt their systems.

Read the rest of this entry »

“Are you sitting on un-mined gold?” asks a tagline for GigaOM’s Structure:Data conference this week. Sybase will help answer that question on Thursday with a workshop led by Director of Business Development David Wiseman.

“Conquering Big Data Analytics in a Heterogeneous World” will consider all five vectors of Big Data analytics by exploring techniques that combine traditional-yet-progressive technologies — such as in-memory technology, column store DBMS and Event Stream Processing — with open source frameworks, such as Hadoop. Properly executed, these arrangements can exploit the full potential of Big Data analytics.

A few tickets for GigaOM’s New York conference were still available at the time of this writing. So register if you haven’t already; check the schedule for Wiseman’s workshop; and stop by the Sybase booth.

TradeTech last week in New York was one of the best yet. David Leinweber started off the proceedings at the not-so-subway-accessible Javits Center, and if you haven’t seen David’s talk about his research at the Lawrence Livermore National Laboratory (yes, where they design bombs), you should see him the first opportunity you have. His examinations of news- and sentiment-based trading strategies are as fascinating as they are valuable.

Several of the sessions incorporated audience polling, and one of the questions piqued my interest: What is the best way to manage high-frequency trading (HFT)?

Read the rest of this entry »

If you can’t run with the big dogs, stay on the porch. It’s one of those idioms that exalt the conspicuously massive, enticing us to celebrate the all-or-nothing spirit of those who dominate.

But big isn’t as easy as it used to be. The U.S. Securities and Exchange Commission is looking into the possibility that some of the big exchanges may have unfairly benefitted their titan clients by spreading out massive orders over numerous smaller exchanges, circumventing discrimination rules.

Read the rest of this entry »

Herbert Hoover aimed to win the U.S. presidency in 1928 by promising prosperity with slogans such as “a chicken in every pot and a car in every garage.” One wonders why houses had garages before cars were ubiquitous, of course.

More seriously, one also wonders about the plausibility of getting a chicken into every pot each night, and the wisdom of promising automobiles to a population mostly lacking mechanical inclination. This was a time when elevators required full-time operators after all.

Following my presentation at the Waters Technology “Big Data Webcast” a couple of weeks ago, someone in the audience asked a question that also got me thinking about logistics: What if data mining spread through an organization, and Hadoop appeared on desktops — populated with lots and lots of data?

Read the rest of this entry »

Capital markets firms are increasing motivated to mine more and more data. So a question about the spread of data caught my attention a couple of weeks ago: Would different groups within an organization collecting as much data as they could for data mining result in multiple copies of data?

I had just presented at the Waters Technology “Big Data Webcast,” which investigated challenges of handling and using Big Data, when a listener made this inquiry during the Q&A.

As the question implies, Big Data should be a company-wide initiative whenever possible. But one group within the company must be responsible for collecting the data. That way other localized expert groups can use that central data source to identify patterns — and avoid spawning multiple data copies.

Read the rest of this entry »

“North Korea’s biggest leader Kim Jung Un, this morning in Beijing time 2:45 a.m., had his residence broken into and was assassinated,” a social media message from China stated last week. “Vehicles are rapidly increasing in number … this sort of battle formation hasn’t been seen in over two years.”

This rumor, and at least one other like it, blasted across microblogging site Weibo before wafting over to its U.S. equivalent, Twitter. Western media skeptically picked up the story, which it later discredited. But rumors such as these can burst a rising market and sink investor confidence.

And a rumor’s effect can intensify based on the stock’s geography and industry.

Read the rest of this entry »

The why of improving bank stress testing is easy. It would improve industry confidence and regulatory compliance. It would also set straight a bunch of Europe’s biggest banks — the ones that failed revised stress testing conducted by Credit Suisse last year.

Tougher and equally compelling is the how. The European banking industry is up against myriad indirect threats. Sybase’s Stuart Grant recently addressed how to improve stress testing with the right mix of technology, standards and integration.

Read the rest of this entry »

Big Data is technology’s hype phrase du jour, bandied incessantly about by analysts and the media. Every vendor seems to have a Big Data product, position or solution.

But what does it mean in capital markets?

It might mean we get to be a bit smug. We have been the leaders in Big Data for several years, and everybody else is simply playing catch up.

“Alright, Neil,” I’m sure you’re thinking, “you’ve put us on a pedestal, now defend it.”

First, let’s look at the patterns in the Big Data talk from analysts and vendors. A large picture emerges that might not be clear from just looking at one analyst or vendor because each has its own area of focus.

But after talking to participants in the financial service industry and reading an array of marketing content from many sources, I have concluded the following:

• Big Data is about lots of data. Buckets of it. Zillabytes, Yadabytes, even Kermitbytes! (sorry – my kids watched The Muppet Movie last night).
• Big Data is about unstructured data. All the data that you can’t be bothered putting into structured format – this is its new home!
• Big Data is about finding value in that sea of unstructured data. That means performing big-time analysis on lots and lots of data to learn precious lessons for your business.
• Big Data is real time. Once you have found something interesting, you can leverage it right away, making your business more agile and ready to pounce on opportunity.

This all sounds epic and intimidating, but the financial services industry has an impressive track record of depending on Big Data and using it with aplomb. What does this sound like?

• Market data volumes are really big. Capital markets firms have used advanced technology for years to store decades of market data for their quants. More than one third of respondents in a Sybase survey indicated that they required more than a decade of market data for analysis and back-testing.
• Finding value in the data is what quants are all about. Devising trading strategies from market analysis has developed from simple strategies, such as paired models in the 1980s, to the intricate gaming strategies of today.
• Trading strategies that include unstructured data (a fun example is Twitter-based trading strategies) are increasingly important. In 2012 we expect to see strong growth in the use of unstructured data in alpha discovery and trading strategies.
• Real-time execution is taken to the physical extremes by collocated high-frequency trading. You don’t get much more real-time than that.

So as an industry, we’ve earned some time in smug mode, watching all of the other verticals catch up with us. But those other verticals will not have to develop this technology as we did because it already exists. They won’t have to reinvent the wheel; they can just learn by watching us.

Big Data’s rising eminence might mean that we can kick back and be smug while they catch up. Or it could mean that we can keep our noses to the grindstone and remain this technology’s vanguard.

The choice is ours.

There’s nothing revolutionary about gauging consumer opinion, except when you use social media to help. Incorporating properly distilled data from the likes of Twitter and LinkedIn offers investors access to an exponentially broader slice of the population.

Imagine going from a survey of hundreds or thousands of people to analyzing data volunteered by millions. It’s like an aggregator on steroids. And that is revolutionary.

A 2010 study in the Cornell University Library found that “collective mood states derived from large-scale Twitter feeds” predicted swings on the Dow Jones Industrial Average with 86.7 percent accuracy. Researchers used two mood tracking tools — similar to what traders could widely employ someday — to gauge the collective disposition of microbloggers.

“Including this mood information leads to higher accuracy,” computational social scientist Johan Bollen told Wired following the study. “We’re presuming on the basis of what we found, if you have some kind of super-duper algorithm and you add our time series, its accuracy will go up.”

And go up it has — at least once. A hedge fund with London-based Derwent Capital Markets beat the market last summer by tallying and categorizing keywords found on Twitter such as “alert,” “happy” and “vital,” according to The Atlantic Wire.

Drilling Down to Individual Stocks

Derwent’s approach reflects a technology in its infancy. It doesn’t try to figure out why the tweets are positive or negative. That makes it suitable only for trading broad market indices such as the Dow.

And tracking social networks still isn’t the best way to predict an individual company’s stock moves. That requires traditional vigilance over SEC filings, the CEO’s state of mind and impending scandals because most social media users don’t brag about their high-performing stocks on Facebook or Tweet about their latest bargain equity.

But they do jabber at length (pun intended) about consumer products via blogs and YouTube, not to mention exposure on Flickr. That opens the door to sophisticated indirect sentiment analysis that may someday predict swings in individual stock prices.

This technique may not be as helpful if you’re tracking the manufacturer of the latest classified military fighting vehicle. But it could be great if you’re watching the manufacturer of the latest smartphone or tablet computer — or the big-box retailer that sells them.

Market sentiment gleaned from examined social media is not yet a crucial variable in the trading equation, as my fellow Trading & Risk Technology blogger Neil McGovern pointed out to tech blog ReadWriteWeb last week. The high price of this nascent analytical technology may be a tough pill for many firms to swallow, but they ignore this type of input at their own detriment.

“Stocks can go up 5 percent or 10 percent [in one] day, which … can often be because of rumors in the market,” McGovern said. “This [technology] seems to make sense to people in the markets as a way to be able to tap into those rumors and help their short-term trading strategies.”

Spotting Corruption, Risk

Monitoring social networks can also spot patterns among seemingly random events, which in turn can expose fraud. Deploying a platform with the right mix of analytical technologies offers firms the ability to uncover market manipulation and respond accordingly.

“[If the technology] is too far off for trading, maybe we would want to look at social media for risk management,” Peter Van Kleef, managing director at Starnberg, Germany-based consultancy Lakeview Capital Market Services told CNBC.com last week. “If we can just find a couple of those time bombs before they go off, we can reduce our exposure to them.”

That means climbing mountains of unstructured data; and social media tracking technology is still learning how to walk. But early adopters are already breaking in their boots, likely to work out the bugs to reap the juiciest data — and create formidable buddy lists along the way.

Even if capital markets firms don’t take up social media tracking en masse, many companies have already started tracking themselves. Facebook, blogs and discussion groups are a few ways that an enterprise can manage its image online.

This image management has evolved from individuals monitoring the Web to automated reports to real-time aware businesses. And it represents a tremendous opportunity for capital markets firms to develop this technology in parallel.

So whether starting small with risk or going big with trade modeling, this is a budding technology with a lot of promise. As we have seen time and time again, failure to adapt soon enough could turn even today’s mightiest firm into the MySpace of trading tomorrow.