scholarly journals Corrigendum to “Investor attention and Google Search Volume Index: Evidence from an emerging market using quantile regression analysis” [Res. Int. Bus. Finance 50 (December) (2019) 1–17]

2022 ◽  
Vol 59 ◽  
pp. 101560
Author(s):  
Vighneswara Swamy ◽  
M. Dharani ◽  
Fumiko Takeda
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Emerging Market ◽  
Volume Index ◽  
Investor Attention ◽  
Search Volume ◽  
Quantile Regression Analysis ◽  
Google Search

Determinants of liquidity in Malaysian SMEs: a quantile regression approach

2018 ◽  
Vol 67 (9) ◽  
pp. 1566-1584 ◽  
Author(s):  
Shaista Wasiuzzaman
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Emerging Market ◽  
Developed Countries ◽  
Data Availability ◽  
Firm Level ◽  
Liquidity Management ◽  
Ols Regression ◽  
Content Type ◽  
Quantile Regression Analysis

PurposeThe management of liquidity has always been seen as a critical but often ignored issue in finance. Despite the abundance of studies on liquidity management, these studies mainly focus on developed countries and on large firms. Liquidity is critical for the small firm but studies on liquidity management in small and medium enterprises (SMEs) are lacking. The purpose of this paper is to examine the firm-level determinants of liquidity of SMEs in Malaysia.Design/methodology/approachData are collected for a total of 986 small firms in Malaysia from 2011 to 2014, resulting in a total of 2,683 observations. Firm-specific variables and the effect of the economy are considered as the possible determinants of liquidity. Ordinary least squares (OLS) regression analysis with standard errors adjusted for firm-level clustering and quantile regression analysis are used for this purpose.FindingsAnalysis using OLS regression technique indicates that a firm’s profitability, its growth, asset tangibility, size, age and firm status are significant factors in influencing its liquidity decision. Leverage and economic condition are not found to have any significant influence on liquidity. However, quantile regression analysis provides a different picture especially for SMEs with liquidity at the quantile levels ofθ=0.10 and 0.90. Atθ=0.10, only profitability, tangibility and firm status are significant, while atθ=0.90, tangibility, size, firm status and, to some extent, age are significant in influencing liquidity levels.Originality/valueTo the author’s knowledge, this is the first study analyzing the liquidity decision of SMEs in an emerging market such as Malaysia. Most studies on liquidity management of SMEs are focused on developed countries due to data availability but these studies are also only a handful. Additionally, this study uses quantile regression analysis which highlights the need to analyze financial decisions at different levels rather than at the aggregate level as done in OLS regression analysis.

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.

scholarly journals Weibo Attention and Stock Market Performance: Some Empirical Evidence

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Minghua Dong ◽  
Xiong Xiong ◽  
Xiao Li ◽  
Dehua Shen
Keyword(s):  
Regression Analysis ◽  
Stock Market ◽  
Quantile Regression ◽  
Empirical Evidence ◽  
Trading Volume ◽  
Market Performance ◽  
Correlation Coefficients ◽  
Investor Attention ◽  
Stock Market Performance ◽  
Quantile Regression Analysis

In this paper, we employ Weibo Index as the proxy for investor attention and analyze the relationships between investor attention and stock market performance, i.e., trading volume, return, and volatility. The empirical results firstly show that Weibo attention is positively related to trading volume, intraday volatility, and return. Secondly, there exist bidirectional causal relationships between Weibo attention and stock market performance. Thirdly, we generally find that higher Weibo attention indicates higher correlation coefficients with the quantile regression analysis.

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 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 INVESTOR ATTENTION AND HERDING IN THE CRYPTOCURRENCY MARKET DURING THE COVID-19 PANDEMIC

2021 ◽  
Vol 10 ◽  
pp. 67-77
Author(s):  
Hajam Abid Bashir ◽  
Dilip Kumar ◽  
K Shiljas
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Vector Autoregression ◽  
Investor Attention ◽  
Search Volume ◽  
Closing Price ◽  
Google Search ◽  
The Impact ◽  
The Relationship

This study examines the relationship between investor attention and herding effects in the cryptocurrency market by employing the vector autoregression and quantile regression models. Furthermore, we examine whether the COVID-19 pandemic affected herding behaviour in cryptocurrencies. Using the daily closing price and Google search volume of the five leading cryptocurrencies, the paper finds that herding in the cryptocurrency market decreases with an increase in investor attention for the overall sample. The results for the COVID-19 period indicate that the impact of investor attention on the herding effect decreases due to increased attention to the pandemic. This study is one of the initial attempts to examine the impact of investor attention on herding in cryptocurrencies.

scholarly journals Measurement of Investor Sentiment and Its Bi-Directional Contemporaneous and Lead–Lag Relationship with Returns: Evidence from Pakistan

2018 ◽  
Vol 11 (1) ◽  
pp. 94 ◽  
Author(s):  
Mehwish Khan ◽  
Eatzaz Ahmad
Keyword(s):  
Emerging Market ◽  
Investor Sentiment ◽  
Volume Index ◽  
Sufficient Evidence ◽  
Market Returns ◽  
Search Volume ◽  
Irrational Behavior ◽  
Thin Market ◽  
Google Search

The present study examines bi-directional contemporaneous and lead–lag relationships between investor sentiment and market returns in the emerging market of Pakistan over the period of 2006 to 2016. To measure investor sentiment, the study employs a direct proxy namely Google search volume index (GSVI) and nine other indirect proxies. Besides conventional regression and VAR model, the study applies Geweke’s (1982) tests to investigate the nature of relationships between sentiment and returns. Thus, the study adds to existing literature by providing latest and thorough statistical evidence on the role of investor sentiment in influencing market returns. The study finds sufficient evidence regarding irrational behavior of investors in the thin market of Pakistan. In particular, the results indicate substantive role of sentiment in dragging stock market away from its sustainable path as implied by economic fundamentals.

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