SEC "Likes" Social Media

If there’s one person in the world who gets the intersection of the social web with investing, it’s Howard Lindzon. So when the SEC ruled Tuesday that company postings on sites like StockTwits/Facebook/Twitter were as good as news releases and company websites (as long as investors were aware of the use of those sites), I immediately turned to Howard for his thoughts. And sure enough, he had a great post today.

One of the most powerful points he made spoke to the fine line that StockTwits walks as a finance social site. They carefully split the difference between 1.) allowing managed /curated content, tools and control necessary for compliance and governance and 2.) enabling the spontaneous, multi-source, lighting-quick conversation paradigm that makes social media so incredible. As he put it:

Rules matter and if they are clearly stated and thoughtfully enforced, communities can thrive (learn, mentor, make a little coin). We [ed. StockTwits] added some basic financial features like the ability to create disclosures/tracking and the removal of the delete function to ensure trust is at the forefront.  No matter what others call us or think, Stocktwits is a NEWSWIRE. Information is flowing from one to many, all day, every day and it is full of context.

The social web will continue to grow and the power of the content being created on that web will continue to impact even the most regulated industries. How other platforms can adapt and fuel that change, like StockTwits has done, will be fascinating to watch.

Check out the full blog here.

Big Boulder: Social Data in the Financial Markets

A panel with Howard Lindzon of StockTwits, Johan Bollen at Guidewave Consulting, Fahad Kamr at Market IQ and Richard Tibbetts of StreamBase having a discussion on why hedge funds, banks and financial services need social data.

Social Data in Financial Markets

A key component of this panel centered on how traders were consuming social data – machine learning vs human consumption. Howard Lindzon kicked off the conversation by comparing social data to the ‘ticker tape’ of stock trading that existed in the 1920s. People would read the orders and watch the tape as a means of gaining insider information. Social data, as Howard sees it, is the new, instant (and legal!) version of the tape.

Tellingly, Howard takes a personal perspective on trading, as opposed to the machine approach, although the opportunities for algorithmic consumption seem clear. As he said, “We have to learn who we are. We’re not Wall Street. StockTwits job is to give users a way to express themselves financially.” And to his point, the human nature of social data consumption is one that is growing in the market.  Seth McGuire of Gnip talked about how traders at investment banks hide their iPhones under their desks because they want to digest the insight coming from Twitter streams, but aren’t allowed by compliance officers to access that data on their desktop.

In defense of machine learning, Johan Bollen discussed his research at Indiana University School of Informatics and Computing. His team focuses on sentiment analysis in social data streams and how overall changes in sentiment can predict the market. His enthusiastic defense of the wisdom of the crowd delved into how algorithms can learn and adapt to changes in that crowd data. In responding to that point, Richard Tibbetts of Streambase raised some interesting questions on the lifespan of alpha within a stream of this nature. It is the nature of an efficient market to correct for alpha – why would a Twitter stream be different?

Fahad Kamr of Market IQ talked about how the newness of social data in the financial world means that “No one really knows what’s up with it”. In the context of Richard’s point on alpha, it will be interesting to see what uses cases retain alpha (sentiment? news-driven?). News-driven and event-trading was particularly discussed, especially in relation to current news and sentiment streams from traditional finance data providers. User reliability will be an important consideration in comparision, as Fahad noted. How do you separate the noise (including those who might try to game the system)?

What the industry requires is actionable insight and a pattern of that insight across time – a common theme across Big Boulder, with interesting discussions on how attendees, panelists and platforms themselves were attempting to provide said insight.

Big Boulder is the world’s first social data conference. Follow along at #BigBoulder, on the blog under Big Boulder, Big Boulder on Storify and on Gnip’s Facebook page.