scholarly journals Investor Attention and Stock Market Activities: New Evidence from Panel Data

2019 ◽  
Vol 7 (2) ◽  
pp. 30 ◽  
Author(s):  
Chaiyuth Padungsaksawasdi ◽  
Sirimon Treepongkaruna ◽  
Robert Brooks
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Trading Volume ◽  
Investor Attention ◽  
Developed Markets ◽  
Search Volume ◽  
Developing Markets ◽  
Prior Literature ◽  
Panel Vector Autoregression ◽  
New Evidence

Using the panel vector autoregression (VAR) method, this paper documents relationships between investor attention and stock market activities; i.e., return, volatility, and trading volume, respectively. In sum, bidirectional dynamic interdependence of the SVI–stock market activities relationship exists, in which the SVI–trading volume relationship shows the strongest evidence. This is consistent with prior literature using trading volume as a proxy of investor attention. However, the relationships in the developed and developing markets are statistically significantly different. The stock markets in the developed markets over-react more to the search volume than those in the developing markets. We postulate that investor attention is one of the key elements in asset pricing in stock markets.

scholarly journals Stock market activity and Google Trends: the case of a developing economy

2019 ◽  
Vol 21 (2) ◽  
pp. 191-212
Author(s):  
Vinh Xuan Bui ◽  
Hang Thu Nguyen
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Design Methodology ◽  
Investor Attention ◽  
Content Type ◽  
Market Activity ◽  
Search Terms ◽  
Search Volume ◽  
Stock Indices ◽  
Google Search

Purpose The purpose of this paper is to investigate the impacts of investor attention on stock market activity. Design/methodology/approach The authors employed the Google Search Volume (GSV) Index, a direct and non-traditional proxy for investor attention. Findings The results indicate a strong correlation between GSV and trading volume – a traditional measure of attention – proving the new measure’s reliability. In addition, market-wide attention increases both stock illiquidity and volatility, whereas company-level attention shows mixed results, driving illiquidity and volatility in both directions. Originality/value To the best of the authors’ knowledge, Nguyen and Pham’s (2018) study has been the only previous study identifying investor attention in Vietnam by using GSV as a proxy and examining the impacts of broad search terms about the macroeconomy on the stock market as a whole – on stock indices’ movements. The paper will contribute to this by quantifying GSV impacts on each stock individually.

scholarly journals Linkages between gold and emerging Asian stock markets: New evidence from the Chinese stock market crash

2021 ◽  
Vol 39 (2) ◽  
Author(s):  
Imran Yousaf ◽  
Shoaib Ali
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Chinese Stock Market ◽  
Stock Market Crash ◽  
Volatility Spillovers ◽  
Hedge Ratios ◽  
The Global Financial Crisis ◽  
New Evidence

This study examines the return and volatility transmission between gold and nine emerging Asian Stock Markets during the global financial crisis and the Chinese stock market crash. We use the VAR-AGARCH model to estimate return and volatility spillovers over the period from January 2000 through June 30, 2018. The results reveal the substantial return and volatility spillovers between the gold and emerging Asian stock markets during the global financial crisis and the Chinese stock market crash. However, these return and volatility transmissions vary across the pairs of stock markets and the financial crises. Besides, we analyze the optimal portfolios and hedge ratios between gold and emerging Asian stock markets during all sample periods. Our findings have important implications for effective hedging and diversification strategies, asset pricing and risk management.

scholarly journals Investor Sentiment and Herding Behavior in the Korean Stock Market

2020 ◽  
Vol 8 (2) ◽  
pp. 34
Author(s):  
Ki-Hong Choi ◽  
Seong-Min Yoon
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Stock Markets ◽  
Trading Volume ◽  
Investor Sentiment ◽  
Herding Behavior ◽  
Absolute Deviation ◽  
Cross Sectional ◽  
Korean Stock Market ◽  
Quantile Regression Method

This paper investigates herding behavior and the connection between herding behavior and investor sentiment. We apply a Cross-Sectional Absolute Deviation (CSAD) approach and the quantile regression method to capture herding behavior in the KOSPI and KOSDAQ stock markets. The analysis results are outlined as follows. First, we find that herding behavior is exhibited during down-market periods in the KOSPI and KOSDAQ stock markets. However, we show that adverse herding behavior occurs in low-trading volume and low-volatility periods. Second, according to the results of the quantile regression, herding behavior is found in the low and high quantiles of the KOSPI and KOSDAQ stock markets. However, adverse herding behavior is also found, which means that investors herd in extreme market conditions. Third, the relationship between investor sentiment and herding behavior is analyzed through regression and quantile regression, and investor sentiment is confirmed to be one of the important factors that can cause herding behavior in the Korean stock market.

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 Investor attention and the response of US stock market sectors to the COVID-19 crisis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lee A. Smales
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Information Discovery ◽  
Investor Attention ◽  
Content Type ◽  
Search Volume ◽  
Global Stock Markets ◽  
Google Search ◽  
The Relationship ◽  
Global Stock

PurposeCOVID-19 has had an immense impact on global stock markets, with no sector escaping its effects. Investor attention towards COVID-19 surged as the virus spread, the number of cases grew and its consequences imposed on everyday life. We assess whether this increase in investor attention may explain stock returns across different sectors during this unusual period.Design/methodology/approachWe adopt the methodology of Da et al. (2015), using Google search volume (GSV) as a proxy for investor attention to examine the relationship between investor attention and stock returns across 11 sectors.FindingsOur results demonstrate that heightened attention towards COVID-19 negatively influences US stock returns. However, relatively speaking, some sectors appear to have gained from the increased attention. This outperformance is centred in the sectors most likely to benefit (or likely to lose least) from the crisis and associated spending by households and government (i.e. consumer staples, healthcare and IT). Such results may be explained by an information discovery hypothesis in the sense that investors are searching online for information to enable a greater understanding of COVID-19's impact on relative stock sector performance.Originality/valueWhile we do not claim that investor attention is the only driver of stock returns during this unique period, we do provide evidence that it contributes to the market impact and to the heterogeneity of returns across stock market sectors.

scholarly journals Multidimensional Liquidity: Evidences from Indian Stock Market

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Sharad Nath Bhattacharya ◽  
Pramit Sengupta ◽  
Mousumi Bhattacharya ◽  
Basav Roychoudhury
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Markets ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Liquidity ◽  
Percentage Error ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Indian Stock Market

Various dimensions of liquidity including breadth, depth, resiliency, tightness, immediacy are examined using BSE 500 and NIFTY 500 indices from Indian Equity market. Liquidity dynamics of the stock markets were examined using trading volume, trading probability, spread, Market Efficiency coefficient, and turnover rate as they gauge different dimensions of market liquidity. We provide evidences on the order of importance of these liquidity measures in Indian stock market using machine learning tools like Artificial Neural Network (ANN) and Random Forest (RF). Findings reveal that liquidity variables collectively explains the movements of stock markets. Both these machine learning tools performs satisfactorily in terms of mean absolute percentage error. We also evidenced lower level of liquidity in Bombay Stock Exchange (BSE) than National Stock Exchange (NSE) and findings supports the liquidity enhancement program recently initiated by BSE.

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 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.

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