The USA Today ran a feature story today on investment firms that are trying to mine Twitter for insight as to how the market will move, and thereby improve investment returns. What's the logic here? Apparently, some experts have found that rigorous analysis of tweets can yield insight as to people's emotional state. Experts then believe those emotions drive investment behavior.
These investment firms cite the research conducted at Indiana University last year by Johan Bollen, a professor of informatics. He found a correlation between the collective mood, as determined by an analysis of millions of tweets, and the movement of the Dow Jones average in subsequent days. Bollen reports an 87% accuracy rate for his algorithms which use Twitter mood measurements to predict the DJIA over the next 3-4 days. Other research focuses on specific companies. Arthur O'Connor, a doctoral candidate at Pace University, has performed a research study which found a positive correlation between social media popularity of major brand names and the performance of those firms' stock prices.
While one might doubt the findings of these particular studies, the overall trend bears watching. More and more investors will try to glean insights from this abundance of data that is available online. Some algorithms will be better than others, but ignoring the data altogether surely cannot make sense. Information is power in the investment community, and social media does provide a great deal of data that may be relevant. The key question is how to mine that dataset most effectively, and then how to build the best predictive algorithms.