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Author(s):  
Darnis Darnis ◽  

This study aims to determine the Influence of Google Search Intensity on the Stability of the Indonesian Capital Market, as well as looking at the defense and security aspects, especially the economy. The research sample is the shares of the banking sector companies listed on the Indonesia Stock Exchange for the 2016-2018 period. The independent variable used in this study is the Abnormal Search Volume Index. The control variables used are Volatility, and Abnormal Trading Volume Lagged. The dependent variable used is Abnormal Trading Volume. The sampling method used in this study used a purposive sampling technique. Obtained the number of samples as many as 18 companies. The analysis technique used in this research is panel data regression. The results of this study indicate that the intensity of Google searches using the ASVI proxy has a significant positive effect on the stability of the stock represented by Abnormal Trading Volume. This illustrates that the use of Google search intensity data can be used as a reference in making defense policies against non-military threats, especially the stability of the Indonesian Capital Market.


2021 ◽  
Vol 14 (11) ◽  
pp. 512
Author(s):  
Bodo Herzog ◽  
Lana dos dos Santos

This paper studies the power of online search intensity metrics, measured by Google, for examining and forecasting exchange rates. We use panel data consisting of quarterly time series from 2004 to 2018 and ten international countries with the highest currency trading volume. Newly, we include various Google search intensity metrics to our panel data. We find that online search improves the overall econometric models and fits. First, four out of ten search variables are robustly significant at one percent and enhance the macroeconomic exchange rate models. Second, country regressions corroborate the panel results, yet the predictive power of search intensity with regard to exchange rates vary by country. Third, we find higher prediction performance for our exchange rate models with search intensity, particularly in regard to the direction of the exchange rate. Overall, our approach reveals a value-added of search intensity in exchange rate models.


2021 ◽  
pp. 232102222110464
Author(s):  
Subhasish Dey ◽  
Jessie Davidson

This paper examines the determinants of non-COVID excess deaths during the COVID pandemic between January and June 2020. These are the extra deaths occurring during the pandemic which are not directly attributable to COVID. While emerging literature examines the determinants of COVID deaths, few look at non-COVID excess deaths, though early estimates suggest they are enough to be seen as a pandemic in their own count. We investigate the impact of factors including COVID deaths and cases, lockdown stringency, economic support and search intensity for non-COVID conditions on excess deaths, using Fixed Effects and GMM estimations. We also use quantile regression to assess the differential impacts of the variables at different stages of the excess death distribution. First, we find that excess deaths are increasing and concave in COVID deaths, and that the rate of growth, as well as the level, of COVID deaths has a significant and positive impact. Second, we find some evidence that stringency of lockdown increases excess deaths by a maximum of 16 extra per-million population. Third, we find a reduction in search intensity for other conditions significantly increases excess deaths, implying that policymakers should ensure public health messaging for other conditions during a pandemic. JEL Classifications: I12, I18, I11


Author(s):  
Pedro Garcia-del-Barrio ◽  
J. James Reade

AbstractThe literature acknowledges “Uncertainty of Outcome” (UO) as a major factor to explain the degree of interest that sporting competitions draw from fans and the general public. Uncertainty about the championship winner is crucial insofar as financial success depends on the capacity to attract potential consumers of spectacle. This paper focusses on one aspect of UO and examines to what extent reduction in the interest of followers is due to the removal of uncertainty about the world drivers’ champion in Formula One. To study how certainty on the winner undermines the degree of attention generated by the Formula One world drivers’ championship, we rely on two alternative indexes—similar although not identical—reported by Google Trends. Both of these appraisals are computed from data on users’ search intensity in Google, where weekly records are normalised on the relative amount of searches per calendar year. Thus, as dependent variables for the empirical analysis we use two measures: Google Trends News (GTN), to capture the intensity with which individuals search news articles associated; and Google Trends Web (GTW), to get a wider overview based on all kind of Internet contents. The former empirical analysis is carried out on 10 years of available data, while the latter approach estimates the models for a larger period of 14 years. Our empirical strategy includes additionally adopting indicator saturation techniques to address this issue while controlling for outliers.


2021 ◽  
Vol 157 (1) ◽  
Author(s):  
Daniel Goller ◽  
Stefan C. Wolter

AbstractEven though the recession in Switzerland triggered by COVID-19 ultimately remained without consequences for the apprenticeship market, significantly fewer apprenticeship contracts had been signed in the months of the first shutdown in 2020 than in the same months of the previous year. Using daily search queries on the national administrative platform for apprenticeship vacancies from February 2020 until April 2021 as a proxy for the supply of potential apprentices, we find a temporal pattern that coincides perfectly with the development of signed apprenticeship contracts. Furthermore, the analyses show that the initially very strong relationship between the intensity of the politically imposed restrictions to fight the COVID-19 pandemic and the daily search queries diminished over time, leading to a search intensity in March 2021 that was back at pre-pandemic level.


Author(s):  
Joanna Ewa Sycz-Opoń

This paper presents a typology of information-seeking styles exhibited by 52 students of the MA translation and interpreting programme at the University of Silesia, Poland. The typology emerged during the large-scale investigation into trainee translators’ research behaviour occurring during translation of a legal text from English into Polish (Sycz-Opoń 2019). The method of investigation combined observation of students’ recorded performances with a think-aloud protocol (TAP). The case-study analysis brought to light significant variation in student’s information-seeking behaviour, which had gone unnoticed in the aggregate statistical data. Individual differences included students’ source preference, search intensity, level of criticism towards sources, diligence, risk-taking, self-confidence, and source reliance. As a result of the analysis the six research styles emerged: traditionalist, innovator, minimalist, true detective, procrastinator, and habitual doubter. They are presented in this paper with special attention to each style’s strengths, weaknesses and recommended teaching approaches. The results suggest the need for information-seeking training geared towards the diverse needs of individual students.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoling Ren ◽  
Yanyan Li ◽  
JuanJuan Zhao ◽  
Yan Qiang

The conventional tourism demand prediction models are currently facing several challenges due to the excess number of search intensity indices that are used as indicators of tourism demand. In this work, the framework for deep learning-based monthly prediction of the volumes of Macau tourist arrivals was presented. The main objective in this study is to predict the tourism growth via one of the deep learning algorithms of extracting new features. The outcome of this study showed that the performance of the adopted deep learning framework was better than that of artificial neural network and support vector regression models. Practitioners can rely on the identified relevant features from the developed framework to understand the nature of the relationships between the predictive factors of tourist demand and the actual volume of tourist arrival.


2021 ◽  
Vol 7 (3) ◽  
pp. 205630512110492
Author(s):  
Ismael Sanz-Labrador ◽  
Miguel Cuerdo-Mir ◽  
Luis Miguel Doncel-Pedrera

COVID-19 has profoundly disrupted national education systems, affecting the future well-being of school-age children. Bacher-Hicks and colleagues showed that the intensity of the search for online learning resources in the United States doubled with respect to pre-COVID-19 levels. However, areas of the country with higher incomes, better internet access, and fewer rural schools experienced significantly greater increases in search intensity. Using a similar method to study the case of Spain, we analyze the evolution of search intensity for a selection of digital educational resources over the period 2015 to 2021. Special attention is paid to the period of COVID-19 pandemic, namely, March 2020 to June 2021. The findings include a widespread rise in the use of digital resources with respect to the use in the last 5 years, which varies by digital educational resource and region. However, the use of digital resources in Spain does not seem to vary according to family income, at least in relation to open access digital educational resources. In addition, there appears to be a steady decline in search intensity for digital educational resources and a certain degree of substitutability following the surge due to the pandemic and the school closures.


2021 ◽  
Vol 50 (5) ◽  
pp. 104219
Author(s):  
Julia Brennecke ◽  
Wolfgang Sofka ◽  
Peng Wang ◽  
Olaf N. Rank

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