Often one of the largest hurdles with new content in the marketplace is understanding how to derive value. I have seen this issue occur across many types of financial firms. We can show plainly there is alpha resident in our content, but that is not enough. The process of mining value from new content is typically more complex and the answer changes depending on how the data is utilized.
At SMA we see sentiment in our S-Factors as an additive item within a decision model that can enhance the performance or risk profile of a system. The hurdle for providers of this new content is to clearly illustrate multiple areas of value and applications for sentiment. This will reduce the inherent risk of devoting resources to incorporating a new data set in algorithmic decisions and spur on wider adoption.
Recently, we have seen promise in utilizing our S-factors to predict the likelihood of volume increases in a specific security. This makes sense since an increase in positive or negative sentiment for a specific security will likely result in focus and activity in that specific name. Changes of note in these metrics/factors should be a precursor to increased activity in that security.
So our initial findings proved positive. When we look at our history of S-Factors and identify conditions when our SV-Score is greater than 2 or S-Delta is above 2 or lower than -2, we can identify significant changes in the underlying security volume. With these S-factor conditions we found increased volume occurred 78% of the time during the event conditions and for two time periods after the event. The volume increase averaged 2.5 times more than the average of that same time period for that security.
This illustrates my point that sentiment can provide value in multiple areas. In this case, the fund manager or buy side trader will find this a simple and effective tool to increase the capacity of a trading system or reduce transaction costs through managing their execution algorithms. A sell side trader can set alerts to indicate securities active in social media or to advise clients on opportunities to move inventory.
Alpha generation in short term momentum models, option volatility prediction, risk management alerts, short term portfolio selection are some of the initial uses of sentiment. It may be that one use case is ample reason to invest the time to integrate sentiment into your decision model, but this would be short sighted. There is more to be mined and new data is typically where the gold is found.
If interested in more detail on our volume study please download our research note found at http://bit.ly/SMA-research .
SMA – Social Market Analytics