This is a very interesting paper, and well-written. My one curiosity that wasn't fulfilled was the classification error rate on predicting the other four classes besides surge: slight+, no recommendation, slight-, and plunge.

I was surprised at the small time scale at which these experiments were performed. Certainly it is useful for day-trading techniques, but I am curious what the performance would be at larger time scales (say, looking for trends a day or week long).

They used a text/time series alignment window that allowed documents to be predictive up to the time of the trend start. It may be more realistic to give a buffer of time between the last story available and the start of the trend? In other words, if a trend starts at 11 AM, only allow articles before 10:30 AM to be predictive for that trend. I don't know how fast news flows in the stock trading world (I'm sure it's fast), but this may be something to consider.

It's interesting that they cluster the time series data (section 2.1.2) according to slope and confidence, only to realize that they don't need to account for confidence in their model.

- greg