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. We examine the extent that unusual levels of chatter on Twitter about a firm portend an oncoming surge of trading of its stock within the hour, over and above what would normally be expected for the stock for that time of day and day of week. We also compare the findings from our explanatory model to the predictive power of Tweets. Although we find a compelling and potentially informative real-time relationship between Twitter activity and trading volume, our forecasting exercise highlights how difficult it can be to make use of this information for monetary gain.

Multilevel sparse functional principal component analysis.

We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

The impact of hospital utilization on patient readmission rate.

We investigate the issue of patient readmission at a large academic hospital in the U.S. Specifically, we look for evidence that patients discharged when post-operative unit utilization is high are more likely to be readmitted. After examining data from 7,800 surgeries performed in 2007, we conclude that patients who are discharged from a highly utilized post-operative unit are more likely to be readmitted within 72 h. Each additional bed utilized at time of discharge increases the odds of readmission on average by 0.35% (Odds Ratio = 1.008, 95% CI [1.003, 1.012]). We propose that this effect is due to an increased discharge rate when the unit is highly utilized.

Examining the discharge practices of surgeons at a large medical center.

We investigate the discharge practices at a large medical center. Specifically, we look for indications that patients are being discharged sooner because of hospital bed-capacity constraints. Using survival analysis techniques, we find statistically significant evidence to indicate that surgeons adjust their discharge practices to accommodate the surgical schedule and number of available recovery beds. We find higher discharge rates on days when utilization is high. We also find an increased discharge rate on days when more surgeries are scheduled. Our findings suggest that discharge decisions are made with bed-capacity constraints in mind. We discuss possible explanations for this, as well as the medical and managerial implications of our findings.