ABSTRACT
This paper utilizes intraday five-minute stock market indices to investigate the causal relation between global stock market volatility and investor attention measured by the Google search volume index during the COVID-19 pandemic. Using the bi-power variation method proposed by Barndorff-Nielsen and Shephard (2004), we separate the realized volatility into two components: Continuous and Jump. Based on 5,583 stock indices-day observations, we find that investor attention is positively related to the realized volatility and its continuous component, but to a lesser extent to jumps. A growth in confirmed cases is positive to all measures of market volatility. Moreover, when the number of confirmed cases increases, more attentive investors reduce market volatility. Our findings are robust regarding various estimation approaches and are less likely to suffer from omitted variable biases and endogeneity concerns. Understanding the findings revealed in this paper is crucial to regulators and policymakers as warnings of additional risks facing retail investors around the globe over the extremely volatile periods. JEL Codes: G14;G15;G40;G41 © 2023 Institute of Financial Management and Research.
ABSTRACT
Based on the Baidu Index, taking "warehousing"and "warehouse"as the keywords, the Baidu search index of "warehousing"and "warehouse"nationwide is statistically analyzed. It is found that the Baidu search index with "warehousing"and "warehouse"as the keywords has significantly increased before and after the COVID-19 epidemic, which shows that the basic role of logistics warehousing in the national economic and social development is increasingly obvious, and the corresponding demand for logistics warehousing is growing. Based on the big data of Warehouse in Cloud, incomplete statistics of "warehousing demand"of "demand location"in China's provinces are similar to the analysis of differences in the source places (regions and provinces) of different search groups through the "population portrait"of Baidu Index. The "warehousing demand"and "warehousing supply"of the key cities in central and Western China are counted. Focusing on the key cities in central and Western China, the correlation analysis of warehousing rent and demand area is carried out. It is found that, on the one hand, the regional logistics warehousing demand is 3 years (the lease term is less than 1 year or 1-3 years), with intra-period volatility. On the other hand, regional centers (National Central Cities) have absolute advantages in the attraction of regional logistics and warehousing. Furthermore, in recent years, due to the impact of the COVID-19 epidemic and extreme meteorological and geological disasters, the adverse impact on the regional economic and social development will show that the demand for logistics and warehousing will be interrupted, reduced and lagged, and the growth will be restored in subsequent years. The average rent of key cities in Western China is 22.52 yuan/m2·month, the average vacancy rate is 11.65%, and there are 1359 warehouses in the park. The average rent of key cities in the central region is 23.5 yuan/m2·month, the average vacancy rate is 13.86%, and there are 1070 warehouses in the park. From the perspective of rent, Changsha shows the highest rent, while Taiyuan shows the lowest rent. Furthermore, the vacancy rate of Chongqing and Xi'an are the highest and lowest, respectively. There is a correlation between the variable of warehousing rent in 2022 and the total retail sales of consumer goods in 2021 (Spearman correlation coefficient is significant). There is a correlation between the variable of average warehousing demand area in 2019-2021 and the sample of the third industry production value in 2021 and the sample variable of total import and export volume of goods in 2021 (Pearson correlation coefficient is significant). The variable of average warehousing demand area in 2019-2021 and the sample variable of resident population. There is a correlation between the total retail sales of social consumer goods in 2021 (Spearman correlation coefficient is significant). On the one hand, the statistical analysis of big data on the digital warehousing information platform can provide reference for the prediction of supply and demand of logistics warehousing and modern logistics service industry in the high-quality development of the region. On the other hand, the spatial econometric analysis of logistics industry and regional economic growth represented by logistics warehousing needs further research. CCS CONCEPTS •Human-centered computing ∼Collaborative and social computing ∼Collaborative and social computing theory, concepts and paradigms ∼Computer supported cooperative work © 2022 ACM.
ABSTRACT
Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, and it is important to detect early and monitor the disease trend for policymakers to make informed decisions. We explored the predictive utility of Baidu Search Index and Baidu Information Index for early warning of COVID-19 and identified search keywords for further monitoring of epidemic trends in Guangxi. A time-series analysis and Spearman correlation between the daily number of cases and both the Baidu Search Index and Baidu Information Index were performed for seven keywords related to COVID-19 from January 8 to March 9, 2020. The time series showed that the temporal distributions of the search terms "coronavirus," "pneumonia" and "mask" in the Baidu Search Index were consistent and had 2 to 3 days' lead time to the reported cases;the correlation coefficients were higher than 0.81. The Baidu Search Index volume in 14 prefectures of Guangxi was closely related with the number of reported cases;it was not associated with the local GDP. The Baidu Information Index search terms "coronavirus" and "pneumonia" were used as frequently as 192,405.0 and 110,488.6 per million population, respectively, and they were also significantly associated with the number of reported cases (r(s) > 0.6), but they fluctuated more than for the Baidu Search Index and had 0 to 14 days' lag time to the reported cases. The Baidu Search Index with search terms "coronavirus," "pneumonia" and "mask" can be used for early warning and monitoring of the epidemic trend of COVID-19 in Guangxi, with 2 to 3 days' lead time.
ABSTRACT
It has been three years since the initial outbreak of COVID-19 in Wuhan, China, which incurred huge damage both physically and psychologically on human's normal life. As a prevention measure, the lockdown was first adopted by Wuhan, then by a long list of Chinese cities and many other major cities around the world. Lockdown is the most restrictive social distancing strategy, turning out effective in mitigating the spreading of COVID-19 on the community level, which, however, cuts off all social interactions and isolates healthy people from each other. The isolated nature of the lockdown could induce severe mental health issues, forming one major source of depression and domestic violence. Given the potential side effect, a comprehensive investigation based on reliable data sources is needed to evaluate the real psychological impact of COVID-19 lockdown and its evolution over time, particularly in the time when the Omicron variant, known for its low death risk, dominates the pandemic. Based on the Baidu Searching Index data collected for Wuhan and Shanghai, two major cities in China that suffered from long-lasting (over two months) lockdowns in 2020 and 2022, respectively, it is found that the major psychological issue during the lockdown period is not induced by the spreading of COVID-19, but by the execution of lockdown. With the deepening of knowledge about COVID-19 and the decrease in the death risk, the psychological impact of lockdown keeps increasing, while the impact of virus spreading becomes less important and even irrelevant to depression and domestic violence issues. The findings reveal that from the psychological perspective, the negative effect of lockdown already overweighs the positive one, which is especially true for the Omicron variant provided its almost ignorable death risk. Therefore, it is necessary to re-evaluate the yield and cost of lockdown for those countries where the COVID-19 pandemic has not yet come to an end.
ABSTRACT
Background: From the beginning of 2020, the world was plunged into a pandemic caused by the novel coronavirus disease-19 (COVID-19). People increasingly searched for information related to COVID-19 on internet websites. The Baidu Index is a data sharing platform. The main data provided is the search index (SI), which represents the frequency that keywords are used in searches. Methods: January 9, 2020 is an important date for the outbreak of COVID-19 in China. We compared the changes of SI before and after for 7 keywords, including "fever", "cough", "nausea", "vomiting", "abdominal pain", "diarrhea", "constipation". The slope and peak values of SI change curves are compared. Ten provinces in China were selected for a separate analysis, including Beijing, Gansu, Guangdong, Guangxi, Heilongjiang, Hubei, Sichuan, Shanghai, Xinjiang, Tibet. The change of SI was analyzed separately, and the correlation between SI and demographic and economic data was analyzed. Results: During period I, from January 9 to January 25, 2020, the average daily increase (ADI) of the SI for "diarrhea" was lower than that for "cough" (889.47 vs. 1,799.12, F=11.43, P=0.002). In period II, from January 25 to April 8, 2020, the average daily decrease (ADD) of the SI for "diarrhea" was significantly lower than that for "cough", with statistical significance (cough, 191.40 vs. 441.44, F=68.66, P<0.001). The mean SI after January 9, 2020 (pre-SI) was lower than that before January 9, 2020 (post-SI) (fever, 2,616.41±116.92 vs. 3,724.51±867.81, P<0.001; cough, 3,260.04±308.43 vs. 5,590.66±874.25, P<0.001; diarrhea, 4,128.80±200.82 vs. 4,423.55±1,058.01, P<0.001). The pre-SI mean was correlated with population (P=0.004, R=0.813) and gross domestic product (GDP) (P<0.001, R=0.966). The post-SI peak was correlated with population (P=0.007, R=0.789), GDP (P=0.005, R=0.804), and previously confirmed cases (PCC) (P=0.03, R=0.670). The growth rate of the SI was correlated with the post-SI peak (P=0.04, R=0.649), PCC (P=0.003, R=0.835). Conclusions: Diarrhea was of widespread concern in all provinces before and after the COVID-19 outbreak and may be associated with novel coronavirus infection. Internet big data can reflect the public's concern about diseases, which is of great significance for the study of the epidemiological characteristics of diseases.
ABSTRACT
This paper investigates the sleeplessness in Chinese cities during the coronavirus disease 2019 (COVID-19) pandemic. We provide first evidence of a link from daily COVID-19 cases resulting in sleep loss in a panel of Chinese cities. We use Wuhan, which was the first city to be completely locked down, as basis to present the result that sleeplessness has become a considerably serious issue owing to the COVID-19 pandemic. In using the intervention policy of various cities as exogenous shocks, we find that lockdown policies significantly increase the sleeplessness level of Chinese cities. In addition, the severity of COVID-19 pandemic significantly exacerbates the negative effect of lockdown policies on sleep quality in the city. Overall, this study indicates that policy makers should pay more attention to public mental health when citizens recover from COIVD-19 by investigating the unintended consequences of COVID-19 on sleeplessness level of cities.
ABSTRACT
In this paper, we study how different categories of crucial COVID-19 information influence price dynamics in stock and option markets during the period from 01/21/20 to 01/31/21. We present a theoretical model in which the behavioral traders make perceptual errors based on the intensity of sentiment arising from different types of news. In addition to the magnitude and direction of the news and its payoff relevance to security prices, other factors such as fear, emotion, and social media can influence the sentiment level. Using Google search data, we construct novel proxies for the sentiment levels induced by five categories of news, COVID, Market, Lockdown, Banking, and Government relief efforts. If the relative presence of behavioral traders in the stock market exceeds that in the option market, different predictions obtain for the effect of sentiment indices on jump volatility of the VIX index, the S&P 500 index, and the S&P 500 Banks index. We find that the jump component in the VIX index is increasing significantly with COVID index, Market index, Lockdown index, and Banking index. However, only COVID index and Market index increase the jump component of realized volatility of the stock indices (S&P 500 index and S&P 500 Banks index). The Government relief efforts index decreases this jump component. Banking and Lockdown index reduce jump volatility in the S&P 500 index and S&P 500 Banks index, but only with a delay of 5 days. These results are consistent with the predictions of our model.
ABSTRACT
BACKGROUND: A novel coronavirus (COVID-19) caused pneumonia broke out at the end of 2019 in Wuhan, China. Many cases were subsequently reported in other cities, which has aroused strong reverberations on the Internet and social media around the world. OBJECTIVE: The aim of this study was to investigate the reaction of global Internet users to the outbreak of COVID-19 by evaluating the possibility of using Internet monitoring as an instrument in handling communicable diseases and responding to public health emergencies. METHODS: The disease-related data were retrieved from China's National Health Commission (CNHC) and World Health Organization (WHO) from January 10 to February 29, 2020. Daily Google Trends (GT) and daily Baidu Attention Index (BAI) for the keyword "Coronavirus" were collected from their official websites. Rumors which occurred in the course of this outbreak were mined from Chinese National Platform to Refute Rumors (CNPRR) and Tencent Platform to Refute Rumors (TPRR). Kendall's Tau-B rank test was applied to check the bivariate correlation among the two indexes mentioned above, epidemic trends, and rumors. RESULTS: After the outbreak of COVID-19, both daily BAI and daily GT increased rapidly and remained at a high level, this process lasted about 10 days. When major events occurred, daily BAI, daily GT, and the number of rumors simultaneously reached new peaks. Our study indicates that these indexes and rumors are statistically related to disease-related indicators. Information symmetry was also found to help significantly eliminate the false news and to prevent rumors from spreading across social media through the epidemic outbreak. CONCLUSION: Compared to traditional methods, Internet monitoring could be particularly efficient and economical in the prevention and control of epidemic and rumors by reflecting public attention and attitude, especially in the early period of an outbreak.