Hello all, last week I wrote a guest post featured on Dr. Ernest Chan’s blog which highlighted some of my research while working with QTS Capital Management on social media sentiment analysis and its place in financial models. The focus of this research was on how to derive sentiment signals from the labeled StockTwits data. This proved to be possible but not as statistically significant as using natural language processing on all of the StockTwits data, like we do at Social Market Analytics. In addition to performing sentiment analysis on StockTwits, we also use Twitter as a source. In some cases we find the Twitter data to outperform StockTwits. In the Figure below, the same open to close simulation is ran with the Twitter data and results in a 4.8 Sharpe Ratio.
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