Google Search Volume Research Articles

The time-varying causal relationship between the Bitcoin market and internet attention

The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention. Taking the Google search volume index as the measure of internet attention, time-varying Granger causality between the global Bitcoin market and internet attention is examined. Empirical results show a strong Granger causal relationship between internet attention and trading volume. Moreover, they indicate, beginning in early 2018, an even stronger impact of trading volume on internet attention, which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble. Although Bitcoin returns are found to strongly affect internet attention, internet attention only occasionally affects Bitcoin returns. Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices, and internet attention is more likely to lead to returns during Bitcoin bubbles. These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.

Early COVID-19 Government Communication Is Associated With Reduced Interest in the QAnon Conspiracy Theory.

Does inadequate risk communication during uncertain times trigger the rise of conspiratorial ideas? We hypothesize that, where government COVID-19 risk communication started early, as measured by the number of days between the start of the communication campaign and the first case in the country, citizens are less likely to turn to conspiratorial explanations for the pandemic, which typically assign blame to powerful actors with secret interests. In Study 1a, we find strong support for our hypothesis in a global sample of 111 countries, using daily Google search volumes for QAnon as a measure of interest in QAnon, which is a conspiracy theory contending, among other things, that COVID-19 is a conspiracy orchestrated by powerful actors and aimed at repressing civil liberties. The effect is robust to a variety of sensitivity checks. In Study 1b, we show that the effect is not explainable by pre-pandemic cross-country differences in QAnon interest, nor by ‘secular’ rising interest in QAnon amid the pandemic. A one-standard deviation (26.2days) increase in communication lateness is associated with a 26% increase in QAnon interest. In pre-registered Study 2, we find limited support for the proposition that early communication reduces self-reported pandemic-related conspiratorial ideation in a sample of respondents from 51 countries. Overall, our results provide evidence that interest in extreme ideas, like QAnon, are highly responsive to government risk communication, while less extreme forms of conspiracism are perhaps less so.

Information search in times of market uncertainty: an examination of aggregate and disaggregate uncertainty

PurposeThis study explores the association between individual investor information demand and two measures of market uncertainty – aggregate market uncertainty and disaggregate industry-specific market uncertainty. It extends the literature by being the first to empirically examine investor information demand and disaggregate market uncertainty.Design/methodology/approachThis paper constructs a measure of information search by using the Google Search Volume Index and computes measures of aggregate and disaggregate market uncertainty using institutional investors' trading data from Ancerno Ltd. The relation between market uncertainty, as measured by trading disagreements among institutional investors, and information search is analyzed using an OLS (Ordinary Least Squares) regression model.FindingsThis paper finds that individual investor information demand is significantly and positively correlated with aggregate market uncertainty but not associated with disaggregated industry uncertainty. The findings suggest that individual investors may not fully incorporate all relevant uncertainty information and that ambiguity-related market pricing anomalies may be more associated with disaggregate market uncertainty.Research limitations/implicationsThis study presents an examination of aggregate and disaggregate measures of market uncertainty and individual investor demand for information, shedding light on the efficiency of the market in incorporating information. A limitation of our study is that our data for market uncertainty is based on investor trading disagreement from Ancerno, Ltd. which is only available till 2011. However, we believe the implications are generalizable to the current time period.Practical implicationsThis study provides the first concurrent empirical assessment of investor information search and aggregate and disaggregate market uncertainty. Prior research has separately examined information demand in these two types of market uncertainty. Thus, this study provides information to investors regarding the importance of assessing disaggregate component measures of the market.Originality/valueThis paper is the first to empirically examine investor information search and disaggregate market uncertainty. It also employs a unique data set and method to determine disaggregate, and aggregate, market uncertainty.

Impact of COVID-19 on interest in hepato-pancreato-biliary diseases.

An important gap of knowledge exists regarding the public interest in hepato-pancreato-biliary (HPB) diseases during the coronavirus disease 2019 (COVID-19) pandemic. We aimed to understand the public interest in HPB diseases in the COVID-19 era. In this infodemiology study, we performed a comparative analysis of Google search volume of HPB terms in 2020–2021 and compared it to a similar time frame (2016–2019) in 3 periods to assess how trends in patient seeking behavior of HPB terms changed during the course of the pandemic in the USA and worldwide. Our analysis showed a substantial decrease in search volume of HPB diseases and procedure terms early in the pandemic. However, search volumes appeared to revert back to pre-pandemic years closer to the 1-year mark in USA and worldwide. Patients may have initially neglected HPB-related issues during the early phase of the COVID-19 pandemic, which could lead to worse outcomes. While HPB-related terms reverted closer to pre-pandemic levels later in the pandemic, further research is needed to assess the long-term impacts.

Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework

The purpose of this article is to investigate whether oil investor attention (OA), measured by Google search volume, contains incremental information content to predict crude oil futures volatility under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, to account for possible structural breaks and nonlinearity in the relation between OA and crude oil volatility, this article extends HAR-type models with regime switching considerations. The results of parameter estimation and out-of-sample prediction show that the in-sample and out-of-sample performance of HAR-type and Markov switching (MS)-HAR-type models with OA is significantly better than that of their corresponding HAR-type and MS-HAR-type models without OA. Furthermore, our findings suggest that (i) HAR-type-OA models tend to produce better forecasts for the volatility of the crude oil market at short horizons (1-day) compared to HAR-type, MS-HAR-type and MS-HAR-type-OA models. (ii) MS-HAR-type-OA models have the best forecasting performance at relatively long prediction horizons (1-week and 1-month). Therefore, the result suggests that the OA and regime switching specifications have a significant positive impact on volatility predictions and can be useful for improving the performance of HAR-type models.

A Multimetric Evaluation of Online Patient Educational Materials for Breast Implant-associated Anaplastic Large Cell Lymphoma.

To assess understandability, actionability, and readability of online patient educational materials (OPEM) related to breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Search volumes for query terms related to BIA-ALCL including "breast implant associated lymphoma," "breast implant associated anaplastic large cell lymphoma," and "BIA-ALCL" were analyzed in one-week increments and normalized to total Google search volume. The same terms were then queried using an online search engine to identify commonly accessed OPEM on this topic. Understandability and actionability of OPEM were evaluated using the Patient Education Materials Assessment Tool. Grade-level readability was determined using generalized estimating equations, with observations nested within readability metrics from each website. All interval estimates were calculated for 95% confidence. Overall, 24 websites were identified based on search parameters. Of these websites, 11 (45.8%) met criteria for understandability, and 1 (4.2%) met criteria for actionability. Overall, readability ranged from 10.2 to 17.3 for all websites with an average grade level readability of 12.4; 0 websites were written at or below a sixth-grade reading level. Government websites had the highest average grade reading level at 14.0, followed by commercial websites at 13.2, nonprofit websites at 12.0, and then academic/hospital-based websites at 11.5. The quality of available OPEM on BIA-ALCL is limited. Future development of OPEM should be designed with the goal of improving both comprehension and actionability to help reduce patient anxiety and unnecessary clinical appointments related to this disease.

The role of ingroup assortative sociality in the COVID-19 pandemic: A multilevel analysis of google trends data in the United States

This study tested how family ties and religiosity, two extended elements of ingroup assortative sociality, would predict group-level COVID-19 severity in the U.S. and how COVID-19 threat would predict ingroup assortative sociality at a weekly level. Multilevel models which analyzed the state-level archival (e.g., religious participation) and Google trends data (e.g., marriage for family ties; prayer for religiosity) on ingroup assortative sociality showed that religious search volume (from 2004 to 2019) significantly and negatively predicted COVID-19 severity (i.e., shorter time delay of first documented cases, shorter overall doubling times, higher reproductive ratio and higher case fatality ratio) across states (Study 1a) and counties (Study 1b) while search volume for family ties only significantly and negatively predicted county-level COVID-19 severity. Multilevel analyses also found that weekly COVID-19 severity weakly predicted weekly search volume of marriage and religion (Study 2a), but when COVID-19 threat was in the collective consciousness in a given week (i.e., Google search volume for coronavirus within 52 weeks), collective levels of ingroup assortative sociality increased from the previous week (Study 2b). Evidence across studies suggested that religiosity, compared with family ties, could serve a more important role for the U.S. people during the deadly pandemic.

Forecasting the stock returns of Chinese oil companies: Can investor attention help?

This paper builds the investor attention index based on the Google Search Volume Index to forecast the stock returns of three major Chinese oil companies during 2004–2019. It takes into account several different model properties, including the time-varying parameters, regime switching and mixed-data frequency, etc. The empirical results suggest that investor attention helps to forecast the stock returns, and its capacity to forecast the US and Hong Kong stock markets is stronger than that of China's A-share market. For the US and Hong Kong markets, forecasting models with investor attention improves the accuracy by up to 35% compared with the benchmark model; but for the A-share market, the new forecasting models are superior only when it is one-month ahead. In addition, this paper confirms that investor attention does have economic value and can help mean-variance investors to get larger certainty equivalent return.

Firearm-related internet searches as a correlate of future firearm suicides: Cross-correlation analyses of monthly Google search volumes and method-specific suicide rates in the United States

• This is a cross-correlation analysis of monthly Google search volumes and rates of suicide by the three most common methods in the USA. • Box-Jenkins transfer function models, the benjamini-hochberg method, and sensitivity analysis were applied. • The search volume of one firearm-related term ( gun suicide ) correlated with the rate of firearm suicides. • The search volume of one term ( “laid off” ) correlated with the rate of poisoning- and asphyxiation-specific suicide. • Cross-correlations elucidated in this study may provide guidance on development of novel suicide-prevention strategies. No previous study has investigated correlations between monthly Google search volumes (MGSVs) of suicide-related search terms and suicide-method specific monthly suicide rates (MSRs). This study examined if the trends in MGSVs of suicide-related terms preceded the variations in method-specific MSRs. MGSVs of 97 candidate suicide-related terms were obtained by averaging 10 timeseries data per term retrieved from Google Trends. Robust time-series analysis methods were applied to MGSVs and firearm-, poisoning-, and asphyxiation-specific MSRs in the United States between 2004 and 2017. Cross-correlation coefficients between MGSVs and methodspecific MSRs were calculated at lags of −3 to −1 (months). In the main analysis, the Benjamini-Hochberg procedure was applied to determine significant correlations while minimizing falsepositive findings. Afterwards, a sensitivity analysis identified the cross-correlations reproducible in two different time spans. Fifty-six search terms with no invalid MGSV data were analyzed. MGSVs of 14 terms correlated with firearm-, poisoning-, or asphyxiation-specific MSRs in one or more lags. In the sensitivity analysis, two terms consistently showed significant positive cross-correlations: gun suicide (with firearm-specific suicides; lag -3) and “laid off” (with poisoning- and asphyxiationspecific suicides; lag -2). Age- or gender-specific search volumes, lags outside the 1- to 3-month range, non-English searches, and confounding factors of MGSV and MSR were not explored. MGSVs of one firearm-related term ( gun suicide ) correlated with future firearmspecific MSRs. MGSVs of one method-neutral term (“ laid off” ) correlated with future poisoning- and asphyxiation-specific MSRs. These terms may be incorporated in novel nowcasting or predictive models for method-specific suicides.

The Effect of the Investors’ Sentiment on the Trade-Off Between Earnings Management Strategies: The Case of Tunisian Market

The aim of this study was to provide evidence to the benefit of an emerging market on whether and how the prevailing investors’ sentiment influences the earnings management tactics i.e. accrual earnings management and real earnings management. used data related to Tunisian listed firms over the period 2009–2018. We measure the investors’ sentiment index using Google search volume approach. We estimated a recursive equation system to investigate the effect of the investors’ sentiment on the trade-off decision between accrual earnings management and real earnings management. First, in line with the catering theory we found that optimistic period is an opportune occasion that encourages Tunisian managers to upward their earnings whether by accrual earnings management or by real earnings management. Indeed, Tunisian managers are likely to rely on abnormal cashflows. Second, we found that the Tunisian managers, use both techniques as complementary rather than substitutes. However, during high sentiment period, this complementary relation decreases which may be explained by total cost of earnings management tools. Our results give the investors and the financial analysts, within emerging markets, important insights and requires them to adopt necessary adjustments to their expectations when evaluated from an optimistic market’ perspective. During such periods, they should be more cautious to the possible distortions of reported earnings made by managers. Our research differs from previous studies dealing with the implication of behavioral biases emerging contexts, which is still embryonic. Such contexts have their uniqueness regarding the economic, social and political environment making evidence drawn from developed contexts questionable. Second, we address this query in a more comprehensive way. We were interested in examining the effect of the investors’ sentiment on each of earnings management technique taken individually as well as on the possible trade-off between them.

Investor awareness or information asymmetry? Wikipedia and IPO underpricing

AbstractWe use the presence of a Wikipedia article for initial public offering (IPO) firms to test theories of information asymmetry and investor awareness. Although we find limited support for the former, our results provide strong support for theories of investor awareness. Specifically, IPO firms with a Wikipedia article exhibit significantly higher underpricing and offer price revisions than do IPO firms without a Wikipedia article. Investor awareness has positive long‐term effects, including greater analyst following and institutional ownership for up to 3 years after the offering. The effect is robust to firm‐specific Google search volume, news coverage, retail trading intensity, social media activity, propensity score matching, and an instrumental variable approach.

Predicting financial market returns in the presence of health crisis: evidence from conventional and Islamic stock markets

The purpose of this study is to examine the usability of the news about the COVID-19 outbreak as a predictor in financial markets. Index values of 11 different sectors in conventional and Islamic stock markets and the index values obtained from COVID-19 deaths, COVID-19 cases and health news were used for this purpose. News variables indices were calculated through Google search volume (G.S.V.) values obtained from Google trend. The daily data between 19 March 2020 and 27 July 2020 were used in the study for 25 index values in total. Regression analysis was used in the study. According to the study results, COVID-19 deaths, COVID-19 cases and health news used as predictors have higher performance than historical return values in all sectors of both conventional and Islamic financial markets. In addition, Islamic stock markets show more attention to the news about the COVID-19 outbreak than conventional stock markets. Accordingly, COVID-19 deaths, COVID-19 cases and health news can be used as effective predictors in Islamic financial markets.

COVID-19 And the african financial markets : Less infection, less economic impact ?

Has the relatively low number of COVID-19 cases and deaths saved Africa from the disease’s economic and financial consequences ? This article assesses the impact of the pandemic on the volatility of major African stock markets using a panel data model. Like other financial markets worldwide, Africa’s have been characterised by increased volatility during the pandemic. The markets appear to respond to the external shocks caused by the health crisis, and Google search volume activity related to the COVID-19 virus, which is treated here as a proxy for panic and fear, is associated with an increase in market volatility of around 7%. For health data, only increases in confirmed cases appear to impact the stability of African markets, and the relatively low fatality rate has had no influence on market dynamics. However, Political responses are associated with a drop in volatility, while the fear of global financial markets exacerbates it. These results have several implications in terms of risk management.

Internet search, fund flows, and fund performance

• This study uses Google search volume index as a direct measure of investor attention. • Fund investors often engage in attention-driven purchases. • Attention-driven purchases fail to allocate capital for earning abnormal returns. • Attention-induced inflows can help sustain new funds in competitive fund markets. This study uses the Google search volume index as a direct measure of investor attention to explore the connection between attention-grabbing information and fund flows, future performance, and the survivorship of newly issued funds. We find that investors often engage in attention-driven purchases of new funds that have captured their attention online. However, fund investors who conduct internet searches and make attention-driven purchases are less sophisticated and fail to allocate their capital for earning abnormal returns. We also find that attention-induced inflows can help sustain new funds in competitive fund markets via potential mitigation of mergers and liquidations. Our robustness checks show similar results for old funds, but attention-driven fund flows do not enhance the survival of old funds.

State-level Google search volumes for neck and shoulder pain correlate with psychosocial and behavioral health indicators

BackgroundThere is growing interest in passively-generated online search data for health sciences research, but limited use to address musculoskeletal illness. Questions/purposes(1) To determine geographic and seasonal patterns in Google queries for shoulder, knee, hip, and neck pain across the United States, and (2) to quantify the state-level correlation of the volume of online symptom searches with rates of various population health indicators. MethodsUsing Google trends, we identified queries for shoulder, knee, hip, and neck pain in every state of the United States during 2018. We assessed the correlation of state-level Google search volumes with 9 population health indicators extracted from the 2018 Behavioral Risk Factor Surveillance System survey: tobacco use, obesity, diabetes, hyperlipidemia, depression, arthritis, cardiovascular disease, participation in physical activities, and college education. ResultsNeck and shoulder pain were the most common queries in most Southern states, while knee pain was generally the top query elsewhere. Queries for neck and shoulder pain peaked during the late fall and winter, while searches for knee and hip pain peaked in the spring. State-level search volumes for shoulder and neck pain correlated closely with all health indicators––particularly with tobacco use, obesity, cardiovascular disease, hyperlipidemia, participation in physical activities and college education. The only strong correlation of hip pain queries was with self-reported prevalence of arthritis. Knee pain queries were not associated with any of the studied health indicators. ConclusionThis study highlights the potential of search engine data to be utilized as population-level health indicators. The state-level correlation of psychosocial and behavioral health indicators with online search volumes for neck and shoulder pain may reflect the influence of mental and social health on the experience of pain.

Google Search Explains Your Gasoline Consumption!

We show that including Google Search Volume Index (GSVI) for bus and train, as proxies for the willingness of the consumers to use public transportation services, improves the explanatory power of the estimated demand for the United States retail gasoline. We find that gasoline consumption is negatively related to the search for public transportation services. Our results demonstrate a showcase for utilizing real-time data to improve quantitative energy policy analysis.

In search of retail investors: The effect of retail investor attention on odd lot trades

We study the effect of retail investor attention on odd lot trading. We observe that increases in abnormal Google search volume predict odd-lot trading. Importantly, this relationship is share price-sensitive: For stocks priced below $11, an increase in abnormal search volume leads to less odd lot trading. For stocks priced above $46, on the other hand, an increase in abnormal search volume leads to more odd lot trading. For a stock priced at $78 – the mean share price in our sample – a one standard deviation increase of abnormal search volume increases the average share of odd lot trading by 10 basis points. This effect is even stronger for more expensive stocks. Our results are consistent with attention-induced trading when investors face wealth constraints and are robust to alternative channels including news and institutional investor attention.

Relationship between Bitcoin Exchange Rate and Other Financial Indexes in Time Series

The Bitcoin exchange rate (BER) is influenced by many variables such as human speculation and policies and, thus, is dependent on the financial system. The fluctuation of BER submitted has been extensively investigated. However, the correlation analysis of the short- and long-term effects by indicators of online sentiment is unexplored. Therefore, this study establishes a VAR model for BER which provides a framework to the Google search volume index (SVI), the investor fear gauge (VIX), and the S&P500 Index. The findings of the analysis suggest that BER and Google SVI have a Granger causality feedback relationship in both the short- and long-term co-integration equilibrium, and the VIX is significantly related to BER in the long-term co-integration.

The Relationship Between Cancer Incidence and Awareness Activities and Google Online Search Volume in Turkey: A Retrospective Research

Objective: The aim of this study was to investigate the relationship between cancer incidence and Google search volumes for the six common types of cancer in Turkey. A second aim of the research is to determine whether online search activities are affected by awareness months for different cancer types. Study Design: A retrospective study was conducted by comparing the incidence of cancer between 2006 and 2017 for the cancers of lung, breast, stomach, prostate, colon and bladder published by IHME (The Institute for Health Metrics and Evaluation) and the Google Trends Search Volume designed by Google. The relationship between annual cancer incidence and Google Search Volume for selected cancer types was tested by Spearman Correlation analysis. Then, it was tested whether there was a difference in Google Search Volume averages of cancer types by months. Results: Google Search Volume has been found to have a significant and positive relationship with the incidence of stomach, colon and bladder cancers. In addition, it was concluded that cancer online searches were affected by awareness campaigns. Breast cancer had the highest Google search volume in October, which was determined as “Breast Cancer Awareness Month” and colon cancer was high in March, which was determined as “Colon Cancer Awareness Month”. Conclusion: These results indicate that online searches may reflect public awareness of the underlying cancer epidemiology. Enhanced understanding of online search patterns can increase traditional epidemiological surveillance, provide opportunities for targeted patient engagement, and enable the assessment of community awareness activities.

In search of COVID-19 and stock market behavior

The aim of this paper is two-fold. First, we investigate the nexus between investor attention to COVID-19 and daily returns in 59 countries. We use Google Search Volume Index to account for investor attention. Our empirical findings suggest that the search volume of the pandemic is negatively associated with daily returns. The effect was strong in the week that the World Health Organization declared it as pandemic and among advanced countries. Second, we explore the relationship between search volume and market volatility. The findings suggest that COVID-19 sentiment generated excess volatility in the market. Our findings remain robust with alternative specifications.