Real-Time Diffusion of Information on Twitter and the Financial Markets.

Do spikes in Twitter chatter about a firm precede unusual stock market trading activity for that firm? If so, Twitter activity may provide useful information about impending financial market activity in real-time. We study the real-time relationship between chatter on Twitter and the stock trading volume of 96 firms listed on the Nasdaq 100, during 193 days of trading in the period from May 21, 2012 to September 18, 2013. We identify observations featuring firm-specific spikes in Twitter activity, and randomly assign each observation to a ten-minute increment matching on the firm and a number of repeating time indicators.

Cross-trial prediction of treatment outcome in depression: a machine learning approach.

Background: Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.