scholarly journals Can google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Huei-Hwa Lai ◽  
Tzu-Pu Chang ◽  
Cheng-Han Hu ◽  
Po-Ching Chou
Keyword(s):  
Volume Index ◽  
Search Volume ◽  
Retail Investor ◽  
Trading Volumes ◽  
Google Search

scholarly journals Donald Trump, investor attention and financial markets

2020 ◽  
Author(s):  
Tapas Tanmaya Mohapatra ◽  
Monika Gehde-Trapp
Keyword(s):  
Financial Markets ◽  
Volume Index ◽  
Investor Attention ◽  
Wealth Transfer ◽  
Search Volume ◽  
Retail Investors ◽  
Retail Investor ◽  
Google Search ◽  
Source Of Information ◽  
Sheer Number

Information attracts attention but attention is costly. Social media has been at the forefront ofinformation dissipation due to the sheer number of users propagating information in a fast but cheap way. We look into one specific case where Donald Trump’s tweets on companies have had effect on retail investors whose only source of information is internet. We find that retail investor attention spike as indicated by surge in Google Search Volume Index following Donald Trump’s tweet, irrespective of the tone in the tweet. We also find that Trump’s tweet facilitates wealth transfer due to selling from the retail investors followed by buying by the institutional investors in low retail investor attention environment. Finally, we see no effect in intra-day returns for the stocks irrespective of the attention they are receiving.

Investors’ Attention and American Depository Receipts Pricing: Evidence from Indian Stocks

2019 ◽  
Vol 9 (12) ◽  
pp. 381-386
Author(s):  
A Sarath Babu
Keyword(s):  
Volume Index ◽  
American Depository Receipts ◽  
Control Variables ◽  
Search Volume ◽  
Depository Receipts ◽  
Book To Market Ratio ◽  
Google Search ◽  
The Impact

This paper is an attempt to examine the impact of investors’ attention on returns and the traded volume of American Depository Receipts prices for selected ten Indian Stocks. The Google search volume index has been used as a proxy for investors’ attention in this paper. However, factors such as size and book to market ratio were used to indicate as control variables. The results reveal that investors’ attention variable significantly affects ADRs traded volume, but has no impact on the ADR prices.

Investor attention using the Google search volume index – impact on stock returns

2019 ◽  
Vol 11 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Vighneswara Swamy ◽  
Munusamy Dharani
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Volume Index ◽  
Trading Behavior ◽  
Excess Returns ◽  
Investor Attention ◽  
Content Type ◽  
Search Volume ◽  
Indian Stock Market ◽  
Google Search

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.

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 Stock Prices Analysis of State-Owned Enterprise and Non-State-Owned Enterprise in Response to Negative Information Period 2017-2019

2020 ◽  
Vol 15 (6) ◽  
pp. 204
Author(s):  
Raka Daniel Lihardo Sumbayak ◽  
Tony Irawan ◽  
Trias Andati
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Financial Performance ◽  
Stock Prices ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Volume Index ◽  
Search Volume ◽  
State Owned Enterprise ◽  
Google Search

There were bad news affected stock prices, i.e. Fraud and bad financial performance. Fraud on State Owned Enterprises (SOE) listed companies was suspected to have a stronger impact on stock prices compared to Non-SOE issuers. The effect of bad financial performance on Non-SOE issuers was thought to have a stronger impact on stock prices when compared to SOE issuers. This research was conducted on SOE and non-SOE that experienced fraud and bad financial performance from 2017 to 2019. Data analysis was performed with the Google Search Volume Index, Difference Test, and Multiple Linear Regression Analysis. The data from Google Search Volume Index showed that SOE issuers were more searched by the public when compared to Non-SOE issuers in responding to Fraud and bad financial performance. Linear Regression Analysis found that the decline in stock prices of SOE issuers was lower than the Non-SOE issuers in response to Fraud. The decline in stock prices of SOE issuers in response to the bad financial performance in the Property and Finance sectors was lower than the decline in stock prices of Non-SOE issuers. However, the decline in the stock prices of Non-SOE companies in response to the bad financial performance in the Basic Industry sector was lower than the SOE issuers. This could be influenced by SOE stock ownership dominated by the Indonesian government and the existence of a Conservatism Bias.

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

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xun Zhang ◽  
Fengbin Lu ◽  
Rui Tao ◽  
Shouyang Wang
Keyword(s):  
Causal Relationship ◽  
Granger Causality ◽  
Trading Volume ◽  
Volume Index ◽  
Time Varying ◽  
Investor Attention ◽  
Empirical Results ◽  
Search Volume ◽  
Google Search ◽  
Shed Light

AbstractThe 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.

scholarly journals The Effect of Google Search Volume Index on Underpriced IPOs and Divergence of Opinions

2021 ◽  
Vol 36 (1) ◽  
pp. 1
Author(s):  
Megen Chivianti ◽  
Sukmawati Sukamulja
Keyword(s):  
Negative Relationship ◽  
Volume Index ◽  
Absolute Difference ◽  
Limited Information ◽  
Search Volume ◽  
Mechanism Research ◽  
Initial Return ◽  
Other Information ◽  
The Right ◽  
Google Search

Introduction/Main Objectives: The purpose of this paper is to examine the effect of the Google Search Volume Index (GSVI), as the moderating variable, on underpriced IPOs, as the independent variable, on the divergence of opinions, as the dependent variable. Background Problems: A divergence of opinions may arise when an error occurs while estimating the right price due to the unavailability of information or only having limited information. Before a company conducts an IPO, potential investors will look for information about the company and each one may interpret the data differently, which results in disagreements between the investors. The investors’ attention is a disagreement mechanism. Research Methods: This study employs the regression analysis of moderation variables with an absolute difference method (ADM) on a sample of 79 Indonesian companies that conducted an IPO between 2015 and 2019. Finding/Results: This study discovered a negative relationship between the initial return and market-adjusted turnover without an interaction effect in the model. The investors’ attention reduces disagreements about underpriced IPOs in the aftermarket. Conclusion: The result of this study found that investors’ attention reduces disagreements about underpriced IPOs proxied by the initial return, because investors closely monitor other information available on the Internet.

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