A recent startup called SNTMNT has opened its Trading Indicator API, which provides price predictions about the price of S&P 500 stocks. The company says its accuracy is about 64%, which suggests that users of the API will be consolidating these indicators with other sources of information.
The approach is a very detailed level of analysis of Twitter sentiment about the stocks, based on so-called ‘stock tickers’ — references to companies like AT&T, whose ticker is T. Many Twitter users talk about stocks, referring to the companies by ticker, which is preceded in most cases by a dollar sign, as in $T.
Johan Bollan and other researchers published a paper, Twitter mood predicts the stock market, back in 2010, which was widely discussed. Their work suggests that the general mood on Twitter predicts the rise or fall of the daily closing price of the Dow Jones Industrial Average with 86.7% accuracy. SNTMNT is strongly influenced by that work, and is another indicator of the growing possibilities in computational social science.
My prediction is that sentiment analysis at either the macro- or micro-level — predicting stock market aggregate moves or the trends for specific stocks — will become a commonplace over the next few years, one of the most obvious applications of big social data.
Now, if someone could only predict the weather…