World's Largest Hedge Fund Uses Twitter For Real-Time Economic Modeling

The use of Twitter and other social media to predict and trade on or, reflexively, generate interest in various assets is nothing new and has been around for years. Whether or not this strategy works is still unclear: the only "hedge fund" that traded purely on Twitter signals, Derwent Absolute Return, shut down shortly after opening (despite supposedly generating positive returns). Superficially, the thinking behind this made sense: as Venturebeat reported previously, based on research done at Indiana University, published in early 2011, there was a strong correlation between sentiments expressed on Twitter and the direction of the Dow Jones Industrial Average. According to the study, “Twitter mood predicts the stock market”, Twitter sentiment correlates with the ups and downs of the next few days on the DJIA with 87 percent accuracy. A separate study at Pace University in 2011 found that social media could predict the ups and downs of stock prices for three global brands, Starbucks, Coca-Cola, and Nike. And sentiment analysis has become an indispensible part of marketers’ toolkits, thanks to companies like Radian6 and Webtrends.


Expectedly, as more and more amateurs have piled into Twitter, the data stream has been subject to the "Yahoo Finance effect" - there is far too much noise, and not nearly enough actionable signal, especially when one tries to strip away the bias behind any given message (see "Trading Twitter: Where Noise Becomes Signal").


Yet one entity that appears to have found significant functionality in Twitter is none other than the world's biggest hedge fund: Bridgewater.


According to Bridgewater's Greg Jensen, speaking during a recent client conference call, the world's largest asset manager (except for the Fed of course) with one of the best long-term track records in history, has been using "everything that is available" online, from social media to real-time Internet prices, to model economic activity in what is effectively real-time. As Jensen said, "from Twitter and Facebook (and so on) we can capture every time somebody is saying they bought a new car. We could add those up and can compare that to the stats and be really on the pulse of what’s going on with something like auto sales or, similarly, with home sales."


Perhaps even more interesting is Bridgewater's search for equivalents of the famous Billion Prices Projects which tracks real-time prices of goods and services around the globe. Specifically, Bridgewater notes that it uses sites like the "Amazon of India" to track inflation "during a balance of payment crisis on a moment-to-moment basis" and thus confirm if any sharp currency moves have filtered down to end prices just by monitoring the internet.


Bridgewater's end goal: to be "able to track the economy on a day-to-day basis." Which in a world of high frequency trading, in which millisecond responses to stimuli is critical for alpha-generation, is paramount. It perhaps also explains why traditional periodic data releases such as inflation data, car sales and other formerly market moving macro releases, no longer pack the punch they once did when it comes to market response.


So with Bridgewater blazing the trail in real-time data monitoring, it is only a matter of time before all other macro hedge funds engage in the same strategy of near-constant monitoring of all concurrent data feeds.


At which point the next logical question is: how long until competitors begin introducing artificial and misleading noise in the data stream, and attempt to confuse Bridgewater and others' signal translators. And how soon before data analytics firm XYZ comes out with its latest offering: one million fake twitter accounts, all of which are programmed to amplify fake economic signals and confuse Twitter algos that translate signal to trades? For a very hefty price of course...

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