Social media isn’t only about news, opinion and commentary. It’s also a potentially profitable form of raw data, at least if you ask Wall Street.
Many on Wall Street think they can use social media networks to reliably predict how the markets for their investments will trend. One London-based investment manager says it will launch a fund dedicated entirely to using Twitter for investment decisions. The fund, managed by Derwent Capital Markets, has colloquially become known as “the Twitter hedge fund” among some Wall Street insiders. Although the launch has been perpetually delayed, it has an estimated $100 million in capital.
Wall Street’s interest in using social networks is far-reaching. Many other social media platforms are receiving attention from investment managers who are searching for the next edge; looking to slice and dice content from social networks to arrive at meaningful conclusions. They are not traditional Wall Street personalities. They’re not personalities at all. Instead, they’re computer-driven algorithms designed to buy and sell massive volumes of stocks, bonds, options, and other financial instruments on their own. Don’t think Gordon Gekko. Think Watson, but for high finance instead of Jeopardy!
This investment philosophy isn’t platform-centric as much as it’s content-needy. Wall Street doesn’t care which platforms users are on, only that social media continues to be a goldmine for content to analyze. There are three primary reasons why this is happening now.
Social Media and Trading: Three Connections
Despite coming into prominence in the mid-1980s, this type of computer-driven trading –- now termed “high-frequency trading” in industry parlance -– generally receives little attention from the mainstream business press. In 2009, it broke into the news, with The New York Times and The Wall Street Journal among the outlets that published articles meant to explain the technology to a mainstream audience.
The relationship between social media and this kind of trading goes something like this:
- Today, computers make most trading decisions. Some estimate that between 46 and 73% of equity trading volumes in the U.S. are made by these high-frequency systems, which are designed to use computer speed and processing power to crunch massive amounts of data and seek small profits on enormous trading volume. A system that can reliably make a few cents per trade on hundreds of thousands of trades per day is quite valuable to a firm.
- Social networks have unique qualities that make them particularly well-suited for the machines that now dominate Wall Street. The sheer amount of content produced on social networks is enormous and continues to grow. The content production itself is a voluntary act. Even the ways with which social networks interact with one another –- the so-called semantic web -– make content production across multiple networks extremely quick. And of course most social media content is public at some level, especially when a carefully programmed algorithm is doing the searching.
- Other competitive advantages are going away. This growing enthusiasm for social content isn’t so much borne from newness as it is from necessity. As the BBC recently reported, the speed at which high-frequency trading can function is approaching its physical limitation. Speed is a competitive advantage for these platforms but it’s quickly becoming ubiquitous. If and when speed –- called “low-latency connectivity” -– is further commoditized, firms will require a new way to get a leg up on the competition.
These three realities have conspired to create demand for new ways to parse out conclusions from content on social media platforms. Having reliable sets of information -– Wall Street tends to call information “data points” or “market data” –- is centrally important to high-frequency trading platforms. Experimentation in sourcing information from social networks is rising rapidly.
These initiatives are certainly impressive. But they should also raise some eyebrows.
Where Trading Via Social Data May Fall Flat
It should shock no one to learn that these sophisticated models sometimes make mistakes. Recent history is wrought with examples of algorithm-driven trading gone awry, from the shocking collapse of a large hedge fund in the late ’90s to several high-profile market failures in more recent years and probably scores of horrifying details that have gone unreleased.
The problems ironically begin with the very human tendency to jump to conclusions. Paul Rowady, senior analyst with consultancy Tabb Group, says that high-frequency trading platforms can use social media data as trading indicators, either supporting or rejecting an existing hypothesis.
However, it would become problematic, he said, for a trading platform to mistake a trading indicator for a trading signal. Differentiating between indicator and signal is important. Indicators are derived from raw market-related data, such as content on social networks, and serve as inputs to models. Signals, usually based on multiple indicators, are the output of those models and eventually become orders to buy or sell financial instruments.
Wall Street calls the mathematicians and engineers who develop these models “quants,” short for quantitative analysts. They program the models that turn multiple indicators into a specific order.
“Social media can play a role in the form of trading indicators, but at this stage, not in the form of pure trading signals,” he said. “The signal-to-noise ratio for that dataset is simply way too low.”
Figuring out how to determine if a set of social media data is good news or bad news for a particular stock or trade is also problematic. It’s easy enough, said Rowady, to use social networks to capture which stocks will move on abnormally high volume in a given day. Reliably establishing whether they will move higher or lower, however, is another matter entirely.
“You can say, ‘There’s a headline on this ticker, so volatility is going to go up.’ But as to the bias of that volatility, is [the price] going to go up or down? That’s a much harder problem to solve,” he said.
Rowady is referring to the difficulty of measuring negative sentiment on a social network, a familiar barrier for many other industries grappling with social media analytics. Users don’t frequently un-like something on Facebook, and using Twitter to complain about negative consumer experiences is dependent on that user’s personality, not the platform’s architecture.
Where are these blind spots, and how should high-frequency trading platforms treat them? How do algorithms find patterns in language used across the social media sphere while at the same time controlling for false-positives? Those are a few of the questions that are keeping Wall Street from mastering the use of social media to inform high-frequency trading strategies.
Social Content is Unlike Business News
It is incorrect to assume that social media content is similar to breaking business news, which Wall Street has long depended on for trading-related information. Business news, of course, is generally filtered by editors and varying levels of fact-checking, and its impact on the markets is dampened by news cycles and copy deadlines. Generally, everyone gets access to breaking business news at the same time, which is entirely by design. Business news is also reliable in all but the very occasional instance -– a news outlet may report a hoax as fact once in awhile, but the press gets it right far more often than not.
On social networks, content is oftentimes published with little to no editorial standards, and the flow of content is constant. Social networks can disrupt, complement, reinforce, or inform the traditional news cycle, but they do not replace it. Any trading algorithm that replaces news feeds with social content or co-mingles the two is inherently faulty.
Going a step further, it would be fairly straightforward to use fraudulent content to manipulate high-frequency trading platforms that misuse social media data. That’s one reason why the Derwent fund set to launch using Twitter is routinely met with skepticism among some Wall Street insiders.
Realistic Expectations for the Future
For Wall Streeters, however, there are a few reasons to be excited about social media’s potential utility, despite the occasional red flag.
Because social media is a global phenomenon, capturing sentiment in hard-to-reach markets could become easier. Some of these markets don’t have established mainstream press — or, if they do, the press may be censored -– making the technical difference between “real” news and social content immaterial to the strategy.
Certain algorithms can also analyze words in a language-neutral manner. The contextual advertising industry is currently making strides in using multilingual or language-agnostic technology to deploy paid content, and the financial markets probably won’t lag far behind.
Perhaps the biggest potential lies with social media’s ability to reinforce or refute certain signals that ultimately lead to trading decisions. It’s akin to a friend giving the final arm-twist that drives you to order ice cream for dessert, or a helpful mentor saying you probably shouldn’t make that one decision. Social media wouldn’t be an adequate determinant by itself. But paired with all the other available information, it could nudge a set of observations into an actual decision.
If Wall Street ends up using social networks for this purpose, it would represent a shift in the role of automated and high-frequency trading and potentially earn a role for social media in the market.
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