Equity Valuation Process And Price-Volume Relationship On Emerging Stock Markets

This paper examines the stock price?volume relationship in emerging markets throughout the world. Using a vector auto-regression analysis on monthly index data, contrary to evidence reported by Saatcioglu and Starks (1998), we find strong evidence on stock price changes leading trading volume. This finding confirms the evidence reported by studies on many developed markets and the ones recently reported by Moosa et al. (2003) and Chen et al. (2004) on  Commodity futures market.  However, the lack of strong evidence on the well-documented positive absolute price-volume relation may imply that differences in institutions and information flows in emerging markets are important enough to affect the valuation process of equity securities

Internet searches for medical symptoms before seeking information on 12-step addiction treatment programs: A web-search log analysis

© 2019 George Nitzburg, Ingmar Weber, Elad Yom-Tov. Background: Brief intervention is a critical method for identifying patients with problematic substance use in primary care settings and for motivating them to consider treatment options. However, despite considerable evidence of delay discounting in patients with substance use disorders, most brief advice by physicians focuses on the long-term negative medical consequences, which may not be the best way to motivate patients to seek treatment information. Objective: Identification of the specific symptoms that most motivate individuals to seek treatment information may offer insights for further improving brief interventions. To this end, we used anonymized internet search engine data to investigate which medical conditions and symptoms preceded searches for 12-step meeting locators and general 12-step information. Methods: We extracted all queries made by people in the United States on the Bing search engine from November 2016 to July 2017. These queries were filtered for those who mentioned seeking Alcoholics Anonymous (AA) or Narcotics Anonymous (NA); in addition, queries that contained a medical symptom or condition or a synonym thereof were analyzed. We identified medical symptoms and conditions that predicted searches for seeking treatment at different time lags. Specifically, symptom queries were first determined to be significantly predictive of subsequent 12-step queries if the probability of querying a medical symptom by those who later sought information about the 12-step program exceeded the probability of that same query being made by a comparison group of all other Bing users in the United States. Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried. Results: In our sample of 11,784 persons, we found 10 medical symptoms that predicted AA information seeking and 9 symptoms that predicted NA information seeking. Of these symptoms, a substantial number could be categorized as nonsevere in nature. Moreover, when medical symptom persistence was examined across a 1-month time period, a substantial number of nonsevere, yet persistent, symptoms were identified. Conclusions: Our results suggest that many common or nonsevere medical symptoms and conditions motivate subsequent interest in AA and NA programs. In addition to highlighting severe long-term consequences, brief interventions could be restructured to highlight how increasing substance misuse can worsen discomfort from common medical symptoms in the short term, as well as how these worsening symptoms could exacerbate social embarrassment or decrease physical attractiveness

Estimating the Population Impact of a New Pediatric Influenza Vaccination Program in England Using Social Media Content

BACKGROUND: The rollout of a new childhood live attenuated influenza vaccine program was launched in England in 2013, which consisted of a national campaign for all 2 and 3 year olds and several pilot locations offering the vaccine to primary school-age children (4-11 years of age) during the influenza season. The 2014/2015 influenza season saw the national program extended to include additional pilot regions, some of which offered the vaccine to secondary school children (11-13 years of age) as well. OBJECTIVE: We utilized social media content to obtain a complementary assessment of the population impact of the programs that were launched in England during the 2013/2014 and 2014/2015 flu seasons. The overall community-wide impact on transmission in pilot areas was estimated for the different age groups that were targeted for vaccination. METHODS: A previously developed statistical framework was applied, which consisted of a nonlinear regression model that was trained to infer influenza-like illness (ILI) rates from Twitter posts originating in pilot (school-age vaccinated) and control (unvaccinated) areas. The control areas were then used to estimate ILI rates in pilot areas, had the intervention not taken place. These predictions were compared with their corresponding Twitter-based ILI estimates. RESULTS: Results suggest a reduction in ILI rates of 14% (1-25%) and 17% (2-30%) across all ages in only the primary school-age vaccine pilot areas during the 2013/2014 and 2014/2015 influenza seasons, respectively. No significant impact was observed in areas where two age cohorts of secondary school children were vaccinated. CONCLUSIONS: These findings corroborate independent assessments from traditional surveillance data, thereby supporting the ongoing rollout of the program to primary school-age children and providing evidence of the value of social media content as an additional syndromic surveillance tool

Inferring individual attributes from search engine queries and auxiliary information

Internet data has surfaced as a primary source for investigation of different aspects of human behavior. A crucial step in such studies is finding a suitable cohort (i.e., a set of users) that shares a common trait of interest to researchers. However, direct identification of users sharing this trait is often impossible, as the data available to researchers is usually anonymized to preserve user privacy. To facilitate research on specific topics of interest, especially in medicine, we introduce an algorithm for identifying a trait of interest in anonymous users. We illustrate how a small set of labeled examples, together with statistical information about the entire population, can be aggregated to obtain labels on unseen examples. We validate our approach using labeled data from the political domain. We provide two applications of the proposed algorithm to the medical domain. In the first, we demonstrate how to identify users whose search patterns indicate they might be suffering from certain types of cancer. In the second, we detail an algorithm to predict the distribution of diseases given their incidence in a subset of the population at study, making it possible to predict disease spread from partial epidemiological data

Investment Strategies, Performance, And Trading Information Impact

This paper examines a set of investment strategies based on past market information to evaluate performance and trading impact on the Canadian Market. In doing so, we assess whether trading information adds value to the effectiveness of these strategies. Utilizing variant models of four different methodologies, we find strong evidence that supported the Momentum Investment Strategy, which buys past winner stocks and sells past loser stocks. Our evidence did not support Contrarian Investment Strategy, which posits that investors overreact to good and bad news. Our winners’ portfolios outperform our losers’ portfolios.  The Negative Volume Effect Strategy did not work, which is contrary to the Foerster, Prihar and Schmitz (1995) study. We found that winners’ stocks did not reverse in cases of heavy volume; nor did loser stocks reverse in a high volume context. However, we did find that trading information has an impact on stock returns and thus adds value to investment strategies for the 1990 to 2000 investment period.  Investors who combine past price and trading volume information in constructing their investment strategies would achieve higher returns than investors who base their portfolio construction decisions solely on stock prices.University

Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions

Detecting controversy in general web pages is a daunting task, but increasingly essential to efficiently moderate discussions and effectively filter problematic content. Unfortunately, controversies occur across many topics and domains, with great changes over time. This paper investigates neural classifiers as a more robust methodology for controversy detection in general web pages. Current models have often cast controversy detection on general web pages as Wikipedia linking, or exact lexical matching tasks. The diverse and changing nature of controversies suggest that semantic approaches are better able to detect controversy. We train neural networks that can capture semantic information from texts using weak signal data. By leveraging the semantic properties of word embeddings we robustly improve on existing controversy detection methods. To evaluate model stability over time and to unseen topics, we asses model performance under varying training conditions to test cross-temporal, cross-topic, cross-domain performance and annotator congruence. In doing so, we demonstrate that weak-signal based neural approaches are closer to human estimates of controversy and are more robust to the inherent variability of controversies

Nanocomposite Bienzymatic Sensor for Monitoring Xanthine in Wound Diagnostics

This work reports a biosensor for monitoring xanthine for potential wound healing assessment. Active substrate of the biosensor has xanthine oxidase (XO) and horseradish peroxidase (HRP) physisorbed on a nanocomposite of multiwalled carbon nanotubes (MWCNT) decorated with gold nanoparticles (AuNP). The presence of HRP provided a two-fold increase in response to xanthine, and a three-fold increase in response to the nanocomposite. With a sensitivity of 155.71 nA μM−1 cm−2 the biosensor offers a detection limit of 1.3 μM, with linear response between 22 μM and 0.4 mM. Clinical sample analyses showed the feasibility of xanthine detection from biofluids in a lesion site due to diffusion of the analyte into surrounding biofluids. Higher concentrations by three-fold were observed from wound proximity, than away from injury, with an average recovery of 110%. Results show the feasibility of monitoring wound severity through longitudinal measurements of xanthine from injured vicinity