SMA uses derived analytics to forecast more effectively in volatile markets.
Here's the tl;dr
Gathering data for clients is a primary offering for SMA and although predicting exact future events is impossible, they needed a way to stay at the forefront of forecasting. Leveraging the Twitter APIs, SMA built a highly predictive method of social listening, scoring, and aggregating sentiment and intention for their clients, enabling them to more effectively forecast in volatile markets.
If one thing can be said about the financial industry, it’s that a lack of predictability comes with the territory. When it comes to financial markets, one of the greatest things a partner can offer is accurate forecasting to help hedge an investor or institution’s bets, which can help to increase return, and reduce risk. Gathering data for clients is a primary offering for Social Market Analytics, Inc. (SMA) and SMA knows they need to stay at the forefront of forecasting. Though predicting exact future events is impossible, their goal was to discover if Twitter sentiment and trends provide insight into data that can better inform investment decisions.
"Twitter represents the feeling of the pit. When people Tweet, stocks move."
Joe Gits, CEO, Social Market Analytics
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.
SMA scores those influencer accounts, using their content to zero in on what might be important for future investment moves, and then aggregates all of the intentions for professional investors based on their topic of choice. “Another organization can’t come in and make those same inferences or forecasting,” says Joe. “The natural language processing capabilities that SMA created were built for the finance world.”
SMA also delivers out-of-sample data, which is a key differentiator from their competitors. Once they create a metric, it is stored and can be pulled up in their archive. They backtest all of the data to see what kind of alpha would generate historically, and they provide static data to the hedge funds they work with. This data is very important to these firms and makes SMA fundamentally different from their competitors.
Joe recalls his early days on Wall Street as he explains the total benefit of Twitter Data. “It was common knowledge that, before the market moved, you would feel the energy of the pit changing. They could hear the noise increasing prior to a market move. Twitter represents that feeling of the pit. When people Tweet, stocks move.” SMA has used Twitter to build a highly predictive method of social listening, scoring, and aggregating sentiment and intention for their clients. Says Joe, simply, “You need to have access to Twitter’s APIs in order to make business decisions.”
SMA services buy side and sell side financial institutions. Their customers include Fidelity Investments, CBOE, and CME, across all asset classes and many derivative types. For more information, you can contact SMA on their website.
See how others have used the Twitter API.