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Professional investors known as quants use hard facts about companies — share price, EBITDA, and so on — to inform the algorithms that carry out their automated trading strategies. But softer sources of information such as reports and rumors have long proved much harder to quantify.
Now, however, a major change is underway thanks to custom financial applications that treat social media discussions as data, and turn it into hard stats.
“The clear trend we’re seeing is the quantification of qualitative aspects of the world,” Claudio Storelli, who overseas Bloomberg’s app portal, told me last week in New York where he led a presentation on technical analysis applications.
He pointed in particular to Twitter, which throws off millions of data points (“inputs for black box consumption” in Storelli’s words), that can provide big clues about stock movements. Here is a screenshot showing Twitter sentiment about Apple, as parsed by an app called iSense:
The result is that computer-based trading tools are using social media signals not only to react to market events, but to predict them as well.
While Bloomberg has hosted such sentiment analysis tools for some time, Storelli said their use is more prevalent than ever. And this is converging with another trend in big-league investing: applications that let traders who lack a background in math or coding deploy technical analysis or academic theories that have traditionally been the purview of quants.
“Our mission is to eliminate the coding barrier,” he said, saying new applications now allow anyone with a basic knowledge of markets and statistics to apply complex technical theories to real-time events.
One example he cited is an application that lets traders integrate the theories of Tom Demark, who is known for using esoteric mathematical models to predict market timing, into run-of-the-mill financial charts.
Together, the two trends Storelli cited — applications integrating technical analysis and the use of social media sentiment — reflect more widespread access to opposite ends of a spectrum of expertise. On one hand, traders can deploy the knowledge of elite experts while, on the other hand, they can act on the collective hunches of millions of average people on social media.
In practice, of course, these approaches are still far beyond the reach of average investors, in no small part due to Bloomberg’s hefty price tag. But they may also appear to be laying the groundwork for democratizing the tools that supply inside insight into financial markets.
To learn more about how tools powered by big data are changing finance and other industries, join us at Gigaom’s Structure Data event in New York City on March 18.