Before Joe Gits founded SMA, he was working at a major media conglomerate when a colleague at another firm asked if his company had been analyzing or following Twitter Data. As it turns out, they weren’t, but that wasn’t going to stop Joe.
Once he left his company, he began using data from the standard Twitter API. Joe utilized natural language processing to discern sentiment and return information on a spectrum of high and low sentiment about any given topic on Twitter. Operating inside of the API, he could filter through financially relevant accounts for conversation topics and sentiment, and get a sense for the public conversation based on what top trendsetters and kingmakers were discussing. Joe knew then that Twitter Data was worth exploring and evaluating. Using social data in this way became the premise for his new company, SMA.
Knowing that hedge funds were very interested in this type of unstructured data, SMA set out to cater to the financial industry. “Trading firms, money managers, and hedge funds are always looking for new sources to inform their forecasting efforts,” says Joe. “The conversation on Twitter was expanding, and the more people are talking, the more helpful it became.”
Information derived from Twitter Data wasn’t available through other analytics tools, like earning estimates and other fundamental data. It allowed for technical analysis and interpretation of the social signal, providing access to free-flowing threads of conversation about products, companies, securities, and investments. Those Tweets could then be scored for weight and give interested parties a window into sentiment around those securities and investments.
When SMA began, the team knew they would have to convince their clients of the benefits of Twitter Data for their firms. Armed with Twitter Data, SMA was able to offer the analysis that only a handful of hedge funds were doing internally to a much broader audience. Now, because of their level of partnership with Twitter Data, SMA is able to create rules and topics that are inclusions and exclusions of particular data types and Tweets, and they identify who the movers and shakers are in the marketplace that are providing the most accurate analysis on their own.