Can Twitter beat the stock market?
By Phil Pearlman
The views expressed are his own.
Can you really use what shows up on Twitter to beat the stock market? The case for being able to do so rests largely on this October 2010 study, published by Bollen, Mao and Zeng, which measured the correlation between “mood states” derived from largescale Twitter feeds and the Dow Jones Industrial Average over time.
Results of the study suggested to the researchers a correlation and even predictive relationship between Twitter sentiment and directional biases in the large cap stock index and perhaps the broader market as well. The rationale behind the study implies–correctly, I think–that Twitter has scaled enough and tapped into a real-time stream of consciousness massive enough that it might reflect collective near-term sentiment, assuming it can be measured correctly.
In May of this year, the London-based hedge fund Derwent Capital raised $40 million and launched a hedge fund which incorporates a sentiment analysis component, based on the measurements in the study and licensed from Bollen and his colleagues.
Paul Hawtin, manager of the new fund, is taking the right approach by integrating Bollen’s Twitter sentiment analysis with existing trading algorithms. This will allow him to utilize market signals he has experience with and confidence in, while at the same time beginning to incorporate and test the Twitter sentiment-analysis data with real capital at risk. Nevertheless, we will not know how this integration is going until he has established a track record over 1-3 years.
Presumably, there are other funds as well with assets under management much larger than $40 million and with significantly greater resources at their disposal that are not publicizing their Twitter-based sentiment analysis but who are more quietly conducting their own analyses on large scale Twitter feeds. The public will not hear about the results of such research or even that it is occurring, especially if the findings prove of value to these large quantitatively based funds. In addition, these larger funds should obtain meaningful results much more quickly than academia, which by tradition tends to plod along as other researchers converge to retest, refine methodologies and then publish. (At StockTwits, we are currently formulating sentiment measurement models based on our own rich market-focused data which we may choose to iterate at some point in the future.)
Correlating Twitter sentiment and market activity is all well and good. To sophisticated market participants, however, the only variable that really matters is “alpha”–that is, can the Twitter sentiment-analysis algorithm increase excess market return?
Late last year, I predicted that hedge funds would waste no time pouring over Bollen et al’s study. The question I raised, though, still applies to Derwent’s new fund as well as others who are likely applying sentiment analysis of the Twitter API to a market timing algorithm. I wrote:
There is a big difference between discovering a method to measure close to real time collective sentiment and its relationship to market behavior and exploiting it for market gains. For one thing, the methodology described in the study above, while provocative, will require scrutiny from the scientific community, reproduction and refinement.
But, and perhaps more importantly, the individuals who will attempt to trade off of such data will be subject to not only market structural limitations but their own emotions as well.
Aside from the development of the scientific methodology, which I believe is in its early days, the question then becomes how managers control for their own emotions, which inevitably exist within the same sentiment reservoir that is being measured.
It’s an age-old question: can you completely stand apart from the crowd when you are part of it? Even if Twitter turns out to be the best market sentiment tool ever, it doesn’t necessarily provide a new answer.