Historical Accuracy Filter Applied to Sentiment Further Increases Predictive Power

At Social Market Analytics (SMA) we create predictive signals by aggregating the intentions of professional investors as expressed on Twitter.  We have accumulated eight years of out-of-sample data illustrating the predictive nature of the data.  We publish sentiment metrics to illustrate the tone of the current conversation relative to historical conversations.  One of our key metrics is S-Score.  S-Score is effectively a Z-Score, the measure of deviation from the mean.  An |S-Score| > 2 means the current conversation is two standard deviations from the mean over the predefined lookback period. 

 In the prior blog we explored the benefits of SMA patented machine learning algorithms on return characteristics.  In this blog we incorporate rolling back tests on the predictive signal to select portfolio securities.  SMA data is predictive across sectors and industries but as with any factor there are securities that react more predictively than others.  

For chart below we use a rolling one-year accuracy metric for predicting subsequent O-C return.  The faded lines are S-Score values only.  Bolded lines represent a theoretical portfolio with accuracy filters overlaid with S-Score values.  Only select |S-Score| > 2 securities that have moved in the predicted direction 60% of the time over the last year (bold lines).  This is all out-of-sample data. 

S-Score > 2 return values are very similar for accuracy filter and S-Score only.  S-Score < -2 had a large impact from the accuracy filter.   Securities reacting negatively to negative Twitter conversation as measured by S-Score continued to underperform relative to sentiment only.  This is another example of using sentiment combined with other metrics leading to statistically significant predictive signals.   

To see how sentiment can be used in your models ContactUs@SocialMarketAnalytics.com.

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