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Article
Publication date: 12 June 2014

Liwen Vaughan

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search

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Abstract

Purpose

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data.

Design/methodology/approach

The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches.

Findings

The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose.

Research limitations/implications

The study is limited to only one country and to one year of data.

Practical implications

Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data.

Originality/value

This is the first study to establish a relationship between search engine query data and business performance and position data.

Details

Online Information Review, vol. 38 no. 4
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 10 January 2024

Nugroho Saputro, Putra Pamungkas, Irwan Trinugroho, Yoshia Christian Mahulette, Bruno Sergio Sergi and Goh Lim Thye

This paper investigated whether a bank’s popularity and depositors' fear of Google search volume could affect bank deposits and credit.

Abstract

Purpose

This paper investigated whether a bank’s popularity and depositors' fear of Google search volume could affect bank deposits and credit.

Design/methodology/approach

The authors used two different quarterly data from Google Trends and banking data from 2012 Q1 to 2020 Q1. Based on available data, Google Trends data start from 2012. The authors exclude data after 2020 Q1 because the Covid-19 pandemic arguably increased the volume of Internet users due to shifting behavior to online activities. They merged and cleaned the data by winsorizing at 5 and 95 percentiles to avoid any outlier problems, reaching 74 banks in the sample. They used panel data estimation of quarterly data following Levy-Yeyati et al. (2010) and Trinugroho et al. (2020).

Findings

The results show that a higher search volume of a bank’s name leads to higher deposits. A higher search volume of depositor fear reduces deposits and credit. The authors also found that banks with high risk and a high search volume of their name have a significantly lower volume of deposits.

Originality/value

To the best of the authors’ knowledge, not many papers in banking and finance have used Google Trends data to gauge related issues regarding depositors' behavior. The authors have filled a gap in the literature by investigating whether the popularity of Google search and depositors' fear could impact deposits and credit. This study also attempted to establish whether Google Trends data could be a reliable source of information to predict depositors' behavior by using a Zscore to measure bank risk.

Details

Managerial Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 28 September 2012

Bing Pan, Doris Chenguang Wu and Haiyan Song

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

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Abstract

Purpose

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

Design/methodology/approach

The authors used search volume data on five related queries to predict demand for hotel rooms in a specific tourist city and employed three ARMA family models and their ARMAX counterparts to evaluate the usefulness of these data. The authors also evaluated three widely used causal econometric models – ADL, TVP, and VAR – for comparison.

Findings

All three ARMAX models consistently outperformed their ARMA counterparts, validating the value of search volume data in facilitating the accurate prediction of demand for hotel rooms. When the three causal econometric models were included for forecasting competition, the ARX model produced the most accurate forecasts, suggesting its usefulness in forecasting demand for hotel rooms.

Research limitations/implications

To demonstrate the usefulness of this data type, the authors focused on one tourist city with five specific tourist‐related queries. Future studies could focus on other aspects of tourist consumption and on more destinations, using a larger number of queries to increase accuracy.

Practical implications

Search volume data are an early indicator of travelers' interest and could be used to predict various types of tourist consumption and activities, such as hotel occupancy, spending, and event attendance.

Originality/value

The paper's findings validate the value of search query volume data in predicting hotel room demand, and the paper is the first of its kind in the field of tourism and hospitality research.

Details

Journal of Hospitality and Tourism Technology, vol. 3 no. 3
Type: Research Article
ISSN: 1757-9880

Keywords

Article
Publication date: 7 March 2016

Marian Alexander Dietzel

Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve…

Abstract

Purpose

Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts.

Design/methodology/approach

Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability.

Findings

The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes.

Practical implications

The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions.

Originality/value

This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

Details

International Journal of Housing Markets and Analysis, vol. 9 no. 1
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 4 April 2016

Nicole Braun

The purpose of this paper is to analyze the effect of investor sentiment, measured with Google internet search data, on volatility forecasts of the US REIT market.

Abstract

Purpose

The purpose of this paper is to analyze the effect of investor sentiment, measured with Google internet search data, on volatility forecasts of the US REIT market.

Design/methodology/approach

The author uses the S&P US REIT index and collects search volume data from Google Trends for all US REIT. Two different Generalized Autoregressive Conditional Heteroskedastic models are then estimated, namely, the baseline model and the Google augmented model. Using these models, one-step-ahead forecasts are conducted and the forecast accuracies of both models are subsequently compared.

Findings

The empirical results reveal that search volume data can be used to predict volatility on the REIT market. Especially in periods of high volatility, Google augmented models outperform the baseline model.

Practical implications

The results imply that Google data can be used on the REIT market as a market indicator. Investors could use Google as an early warning system, especially in periods of high volatility.

Originality/value

This is the first paper to use Google search query data for volatility forecasts of the REIT market.

Details

Journal of Property Investment & Finance, vol. 34 no. 3
Type: Research Article
ISSN: 1463-578X

Keywords

Article
Publication date: 24 May 2019

Vighneswara Swamy and Munusamy Dharani

The purpose of this paper is to investigate whether the investor attention using the Google search volume index (GSVI) can be used to forecast stock returns. The authors also find…

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Abstract

Purpose

The purpose of this paper is to investigate whether the investor attention using the Google search volume index (GSVI) can be used to forecast stock returns. The authors also find the answer to whether the “price pressure hypothesis” would hold true for the Indian stock market.

Design/methodology/approach

The authors employ a more recent fully balanced panel data for the period from July 2012 to Jun 2017 (260 weeks) of observations for companies of NIFTY 50 of the National Stock Exchange in the Indian stock market. The authors are motivated by Tetlock (2007) and Bijl et al. (2016) to employ regression approach of econometric estimation.

Findings

The authors find that high Google search volumes lead to positive returns. More precisely, the high Google search volumes predict positive and significant returns in the subsequent fourth and fifth weeks. The GSVI performs as an useful predictor of the direction as well as the magnitude of the excess returns. The higher quantiles of the GSVI have corresponding higher excess returns. The authors notice that the domestic investor searches are correlated with higher excess returns than the worldwide investor searches. The findings imply that the signals from the search volume data could be of help in the construction of profitable trading strategies.

Originality/value

To the best of the authors knowledge, no paper has examined the relationship between Google search intensity and stock-trading behavior in the Indian stock market. The authors use a more recent data for the period from 2012 to 2017 to investigate whether search query data on company names can be used to predict weekly stock returns for individual firms. This study complements the prior studies by investigating the relationship between search intensity and stock-trading behavior in the Indian stock market.

Details

Review of Behavioral Finance, vol. 11 no. 1
Type: Research Article
ISSN: 1940-5979

Keywords

Article
Publication date: 3 August 2015

Bixia Xu and Zhulin Huang

This paper aims to examine whether information search frequency of accounting information is related to the explanatory power of accounting information for firm market value. It…

Abstract

Purpose

This paper aims to examine whether information search frequency of accounting information is related to the explanatory power of accounting information for firm market value. It also examines whether information content and state of nature can have an impact on this relationship.

Design/methodology/approach

The paper is an empirical study using Web search volume data collected from Google Trends and financial and market data collected from Compustat.

Findings

This paper finds that investors use Web search engines as an alternative way to search for information they need, search frequency of accounting information is positively related to the explanatory power of accounting information for firm market value, the relationship is found differential between statements and categories within a statement depending on the information content and the relationship is found stronger during economic upturns.

Research limitations/implications

This paper examines 59 accounting items that are cross-firm commonly reported and that have data availability in Compustat. The external validity might be an issue.

Practical implications

This paper is of interest to standard setters, corporate management and academics who wish to understand and improve the value of accounting information in the capital market.

Originality/value

This paper is the first study which provides a comprehensive examination of the impact of investors’ information search volumes on the explanatory power of accounting information. It is also the first paper that intrudes Google Trends search volume data into accounting research.

Details

International Journal of Accounting and Information Management, vol. 23 no. 3
Type: Research Article
ISSN: 1834-7649

Keywords

Article
Publication date: 26 August 2014

Marian Alexander Dietzel, Nicole Braun and Wolfgang Schäfers

The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve…

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Abstract

Purpose

The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.

Design/methodology/approach

This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.

Findings

The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively.

Practical implications

The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions.

Originality/value

This is the first paper applying Google search query data to the commercial real estate sector.

Details

Journal of Property Investment & Finance, vol. 32 no. 6
Type: Research Article
ISSN: 1463-578X

Keywords

Article
Publication date: 13 May 2020

Mariano Gonzalez Sanchez

This empirical work studies the influence of investors’ Internet searches on financial markets.

Abstract

Purpose

This empirical work studies the influence of investors’ Internet searches on financial markets.

Design/methodology/approach

In this study, an asset pricing model with six factors is used, and autoregression, heteroscedasticity and moving average are taken into account to extract the independent shocks of each variable. Subsequently, a causality in-mean and in-variance analysis is performed to test the influence of Google searches on financial market variables, specifically, to test whether there is an influence on the idiosyncratic returns of financial assets.

Findings

Unlike most of the literature, the results show that Google searches on the name of listed companies have little influence on the trend and volatility of asset returns. On the contrary, these searches are shown to have a significant influence on trading volumes in the following week.

Practical implications

When analyzing specific effects, such as the influence of Internet searches, on financial markets, it is necessary that the model must include financial properties (asset valuation models) and statistical characteristics (stylized facts); otherwise, the empirical results could be inconsistent, since, among other issues, statistical findings may not be robust given autocorrelation and heteroscedasticity, and if an asset valuation model is not considered, the specific effect analyzed could simply be an indirect effect of a risk factor excluded from the model.

Originality/value

The empirical evidence shows that individual investors using Google have a significant influence on volume only so that institutional investors using other sources of information drive market prices. This means that potential investors should only be interested in the Internet searches index if their interest is focused on trading volume

Details

Review of Behavioral Finance, vol. 13 no. 2
Type: Research Article
ISSN: 1940-5979

Keywords

Article
Publication date: 2 October 2017

Partha Mukherjee and Bernard J. Jansen

It is important to measure the interaction between conversing in social media and searching on the web in order to understand the impact on electronic word-of-mouth marketing. The…

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Abstract

Purpose

It is important to measure the interaction between conversing in social media and searching on the web in order to understand the impact on electronic word-of-mouth marketing. The paper aims to discuss this issue.

Design/methodology/approach

The authors research the relationship between social media conversing and web searching concerning brands on three major social soundtrack platforms (Instagram, Twitter, and Tumblr) and on a major web search engine (Google). The authors examine the effects of changes in both volume and attitude of conversing and volume of searching for two phases (Pre and Post) concerning brands in commercials aired during Super Bowl XLIX. The authors perform Granger causality testing and panel data regression analysis to determine the causal relationship between social media conversing and web searching.

Findings

Results show that volume and attitude of social media conversing has a significant causality relationship to the volume of web searching. Each unit increase of volume on Twitter, Instagram, and Tumblr significantly increases Google search volume for the same brands by 4.7 times, 11.9 times, and 8.7 times, respectively. Each unit increase of attitude score on Twitter significantly increases web search volume 3.96 times, while for Tumblr, search volume significantly increases 0.95 times with each unit. Interestingly, search volume also has a significant causality relationship on the volume of social media postings.

Originality/value

This research seeks to understand the commercial impacts of the interaction among broadcast advertising, social media conversing, and web searching for which there is limited prior work, especially in the context of a major media event.

Details

Internet Research, vol. 27 no. 5
Type: Research Article
ISSN: 1066-2243

Keywords

1 – 10 of over 6000