A STUDY ON AFTER-TEMPORAL ASSOCIATION BETWEEN ONLINE SEARCH VOLUME AND STOCK PRICE WITH AN INTELLIGENT ATARII METHOD

2014 ◽  
Author(s):  
CHANGYU WANG ◽  
QIANG WEI ◽  
XUNHUA GUO ◽  
GUOQING CHEN
Keyword(s):  
Stock Price ◽  
Temporal Association ◽  
Online Search ◽  
Search Volume

scholarly journals Google search behavior for meningitis and its vaccines: an infodemiological study

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
John Angelo Luigi S. Perez ◽  
Adrian I. Espiritu ◽  
Roland Dominic G. Jamora
Keyword(s):  
Influenza Vaccine ◽  
Pneumococcal Vaccine ◽  
Rank Correlation ◽  
Infectious Agents ◽  
Online Behavior ◽  
Online Search ◽  
Search Volume ◽  
Search Patterns ◽  
Google Search ◽  
The Relationship

Abstract Background The internet has made significant contributions towards health education. Analyzing the pattern of online behavior regarding meningitis and vaccinations may be worthwhile. It is hypothesized that the online search patterns in meningitis are correlated with its number of cases and the search patterns of its related vaccines. Methods This was an infodemiological study that determined the relationship among online search interest in meningitis, its worldwide number of cases and its associated vaccines. Using Google Trends™ Search Volume Indices (SVIs), we evaluated the search queries “meningitis,” “pneumococcal vaccine,” “BCG vaccine,” “meningococcal vaccine” and “influenza vaccine” in January 2021, covering January 2008 to December 2020. Spearman rank correlation was used to determine correlations between these queries. Results The worldwide search interest in meningitis from 2008 to 2020 showed an average SVI of 46 ± 8.8. The most searched topics were symptoms, vaccines, and infectious agents with SVIs of 100, 52, and 39, respectively. The top three countries with the highest search interest were Ghana, Kazakhstan, and Kenya. There were weak, but statistically significant correlations between meningitis and the BCG (ρ = 0.369, p < 0.001) and meningococcal (ρ = 0.183, p < 0.05) vaccines. There were no statistically significant associations between the number of cases, influenza vaccine, and pneumococcal vaccine. Conclusion The relationships among the Google SVIs for meningitis and its related vaccines and number of cases data were inconsistent and remained unclear. Future infodemiological studies may expand their scopes to social media, semantics, and big data for more robust conclusions.

scholarly journals Did an Ebola outbreak influence the 2014 U.S. federal elections? (Hint: Only if you ignore autocorrelation)

2016 ◽  
Author(s):  
Leonid Tiokhin ◽  
Daniel Hruschka
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Online Search ◽  
First Order ◽  
Search Volume ◽  
Temporal Autocorrelation ◽  
Analytical Strategies ◽  
Existing Data

In a recent paper, Beall, Hofer, and Schaller (2016) use observational time series data to test the hypothesis that the 2014 Ebola outbreak influenced the 2014 U.S. Federal Elections. They find substantial associations between online search volume for Ebola and people’s tendency to vote Republican, an effect observed primarily in states with norms favoring Republican candidates. However, the analyses do not deal with the well-known problem of temporal autocorrelation in time series. We show that all variables analyzed exhibit extremely high levels of temporal autocorrelation (i.e. similarity in data-point values across time). After appropriately removing first-order autocorrelation, the observed relationships are attenuated and non-significant. This suggests that either no real associations exist, or that existing data are insufficiently powered to test the proposed hypotheses. We conclude by highlighting other pitfalls of observational data analysis, and draw attention to analytical strategies developed in related disciplines for avoiding these errors.

scholarly journals Relationship Between State-Level Google Online Search Volume and Cancer Incidence in the United States: Retrospective Study

2018 ◽  
Vol 20 (1) ◽  
pp. e6 ◽  
Author(s):  
Charles A Phillips ◽  
Allison Barz Leahy ◽  
Yimei Li ◽  
Marilyn M Schapira ◽  
L Charles Bailey ◽  
...  
Keyword(s):  
United States ◽  
Retrospective Study ◽  
Cancer Incidence ◽  
State Level ◽  
The United States ◽  
Online Search ◽  
Search Volume

scholarly journals Did an Ebola outbreak influence the 2014 U.S. federal elections? (Hint: Only if you ignore autocorrelation)

2016 ◽  
Author(s):  
Leonid Tiokhin ◽  
Daniel Hruschka
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Online Search ◽  
First Order ◽  
Search Volume ◽  
Temporal Autocorrelation ◽  
Analytical Strategies ◽  
Existing Data

In a recent paper, Beall, Hofer, and Schaller (2016) use observational time series data to test the hypothesis that the 2014 Ebola outbreak influenced the 2014 U.S. Federal Elections. They find substantial associations between online search volume for Ebola and people’s tendency to vote Republican, an effect observed primarily in states with norms favoring Republican candidates. However, the analyses do not deal with the well-known problem of temporal autocorrelation in time series. We show that all variables analyzed exhibit extremely high levels of temporal autocorrelation (i.e. similarity in data-point values across time). After appropriately removing first-order autocorrelation, the observed relationships are attenuated and non-significant. This suggests that either no real associations exist, or that existing data are insufficiently powered to test the proposed hypotheses. We conclude by highlighting other pitfalls of observational data analysis, and draw attention to analytical strategies developed in related disciplines for avoiding these errors.

scholarly journals Google Search and Stock Market Performance: Evidence from Malaysia

2019 ◽  
Vol 2 (1) ◽  
pp. 49-55
Author(s):  
Kelvin Yong Ming Lee
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Trading Volume ◽  
Market Performance ◽  
Volume Index ◽  
Investment Strategies ◽  
Search Volume ◽  
Stock Market Performance ◽  
Google Search ◽  
The Relationship

Nowadays, the internet changes the way for information searching and processing. Along with that, Google search had become the most popular search engine on the web since it allows users access to the information at a minimal cost. This study intends to investigate the relationship between Google search volume and the Malaysian stock market performance in the aspects of returns, volatility, and trading volume. The sample of this study consisted of 29 listed companies from the Malaysian stock market. The sample period of this study covered the period from 2016 to 2018. The data related to the stock market were downloaded from Investing.com, whereas the data related to Google search were downloaded from the database of Google Trend. The results indicated that the Google search volume index (GSVI) of the previous week tends to have significant positive impacts on the stock price changes. Thus, a higher search volume of the specific company name tends to increase the stock price of the particular company in the following week. Besides that, this study also revealed that the stock market performance tends to be affected by stock market performance in the previous week. Lastly, this study suggested that signals of GSVI need to be included in the investment strategies.  

scholarly journals Did an Ebola outbreak influence the 2014 U.S. federal elections? (Hint: Only if you ignore autocorrelation)

2016 ◽  
Author(s):  
Leonid Tiokhin ◽  
Daniel Hruschka
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Online Search ◽  
First Order ◽  
Search Volume ◽  
Temporal Autocorrelation ◽  
Analytical Strategies ◽  
Existing Data

In a recent paper, Beall, Hofer, and Schaller (2016) use observational time series data to test the hypothesis that the 2014 Ebola outbreak influenced the 2014 U.S. Federal Elections. They find substantial associations between online search volume for Ebola and people’s tendency to vote Republican, an effect observed primarily in states with norms favoring Republican candidates. However, the analyses do not deal with the well-known problem of temporal autocorrelation in time series. We show that all variables analyzed exhibit extremely high levels of temporal autocorrelation (i.e. similarity in data-point values across time). After appropriately removing first-order autocorrelation, the observed relationships are attenuated and non-significant. This suggests that either no real associations exist, or that existing data are insufficiently powered to test the proposed hypotheses. We conclude by highlighting other pitfalls of observational data analysis, and draw attention to analytical strategies developed in related disciplines for avoiding these errors.

scholarly journals Search trends of online interest in LUTS enquiry, diagnosis and treatment in China (Preprint)

2021 ◽  
Author(s):  
Shanzun Wei ◽  
Ming Ma ◽  
Changjing Wu ◽  
Botao Yu ◽  
Lisha Jiang ◽  
...  
Keyword(s):  
Regional Scale ◽  
Age Distribution ◽  
Mainland China ◽  
Online Search ◽  
User Interest ◽  
Search Volume ◽  
Trend Data ◽  
And Gender ◽  
China Mainland ◽  
Search Index

BACKGROUND Lower urinary tract symptoms (LUTS) are one of the most described urination disorders worldwide. Previous investigations have focused predominantly on the prospective identification of cases that meets the researchers criteria, the genuine demand as regard to LUTS and related issues from patients may thus be neglected. OBJECTIVE To examine the online search trend and behaviours related to LUTS on a national and regional scale using the dominant major search engine in mainland China. METHODS The Baidu Index was queried using the LUTS related terms for the period 2011.01–2020.09. The search volume for each term was recorded to analyze the search trend and demographic distributions. For user interest, the data of demand graph and trend data were collected and analyzed. RESULTS Of the 13 LUTS symptom domains, 11 domains are available in Baidu index database. The BSI for each LUTS domains varies from 37.78% to 1.47%. the search trend of urinary frequency (APC = 7.82%; p < .05; 2011-2018), incomplete emptying (APC = 17.74%; p < .05; 2011-2016), nocturia (APC = 11.54%; p < .05; 2011-2018), dysuria (APC = 20.77%; p < .05; 2017-2020) and incontinence (APC = 13.39%; p < .05; 2011-2016). The search index trends for the weak stream (APC = -4.68%; p < .05; 2011-2017, APC = 9.32%, p = 2.35, 2017-2020), split stream (APC = 9.50%; p = .44, 2011-2013, APC = 2.05%, p = .71, 2013-2020), urgency (APC = -2.63%; p = 1.17, 2011-2018, APC = 8.58%; p = .19, 2018-2020), nocturnal enuresis (APC = -4.20%; p = .62, 2011-2017, APC = 20.77%, p < .05, 2017-2020). The age distribution of the population of each LUTS symptom enquiries shows that the population aged 20 to 40 years comprised over 65% of the total search enquiries. Seconded is 5.2%- 21.50 % in the age group 40-49 years. People from the east part of china made over 50% of the total search queries. Further, most of these searches for LUTS symptom entries are related to those for urinary diseases in varying degrees. CONCLUSIONS Online interest in LUTS term fluctuated wildly and was reflected timely by Baidu index in china mainland. The online search popularity for each LUTS terms varie significantly and is differed from personal interest, population concerns, regional variations and gender. These data can be used by providers to track LUTS prevalence and population interests in guiding establish disease-specific healthcare policies and optimize the physician-patient healthcare sessions.

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