The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice

<p>Abstract</p> <p>Background</p> <p>In patients with osteoarthritis, a detailed assessment of degenerative cartilage disease is important to recommend adequate treatment. Using a representative sample of patients, this study investigated whether MRI is reliable for a detailed cartilage assessment in patients with osteoarthritis of the knee.</p> <p>Methods</p> <p>In a cross sectional-study as a part of a retrospective case-control study, 36 patients (mean age 53.1 years) with clinically relevant osteoarthritis received standardized MRI (sag. T1-TSE, cor. STIR-TSE, trans. fat-suppressed PD-TSE, sag. fat-suppressed PD-TSE, Siemens Magnetom Avanto syngo MR B 15) on a 1.5 Tesla unit. Within a maximum of three months later, arthroscopic grading of the articular surfaces was performed. MRI grading by two blinded observers was compared to arthroscopic findings. Diagnostic values as well as intra- and inter-observer values were assessed.</p> <p>Results</p> <p>Inter-observer agreement between readers 1 and 2 was good (kappa = 0.65) within all compartments. Intra-observer agreement comparing MRI grading to arthroscopic grading showed moderate to good values for readers 1 and 2 (kappa = 0.50 and 0.62, respectively), the poorest being within the patellofemoral joint (kappa = 0.32 and 0.52). Sensitivities were relatively low at all grades, particularly for grade 3 cartilage lesions. A tendency to underestimate cartilage disorders on MR images was not noticed.</p> <p>Conclusions</p> <p>According to our results, the use of MRI for precise grading of the cartilage in osteoarthritis is limited. Even if the practical benefit of MRI in pretreatment diagnostics is unequivocal, a diagnostic arthroscopy is of outstanding value when a grading of the cartilage is crucial for a definitive decision regarding therapeutic options in patients with osteoarthritis.</p

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment