Investor attention is a risk pricing factor? Evidence from Chinese investors for self-selected stocks

2019 ◽  
Vol 10 (1) ◽  
pp. 95-112 ◽  
Author(s):  
Dayong Dong ◽  
Keke Wu
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Significant Risk ◽  
Chinese Stock Market ◽  
Investor Attention ◽  
Recognition Mechanism ◽  
Content Type ◽  
Exposure Factor ◽  
Risk Pricing

Purpose The purpose of this paper is to empirically examine whether investor attention is a significant risk pricing factor. Design/methodology/approach Using investor attention data from Eastmoney.com, which provides for each stock the number of investors whose watch list includes that stock on a daily basis, this paper constructs a “heat” factor based on the change in investor attention and a “market exposure” factor based on the proportion of attention on a given stock over the attention to all stocks. Using the Fama−MacBeth two-step regression and a rolling analysis, this study examines the ability of the investor attention factor to explain market returns. Findings The empirical results show that there exists a risk premium for the “heat” factor and “market exposure” factor that is significantly different from zero. This finding shows that investor attention can systematically influence stock returns, making it a significant risk pricing factor. Practical implications This paper’s research on the risk pricing factors of investor attention can help investors to rationally build investment portfolios, avoid risks and form a sound investment concept, which will further reveal the information recognition mechanism of the capital market and standardize the information disclosure behavior of listed companies. Originality/value This paper provides evidence that investor attention is a risk pricing factor for the stock market. There are “heat” factors and “market exposure” factors in the Chinese stock market that significantly affect the purchasing behavior of individual investors.

Investor attention and market correction

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhongdong Chen
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Behavior ◽  
Investor Attention ◽  
Information Effect ◽  
Content Type ◽  
Economic Significance ◽  
New Information ◽  
Positive Volume ◽  
The Impact

PurposeThis study disentangles the investor-base effect and the information effect of investor attention. The former leads to a larger investor base and higher stock returns, while the latter facilitates the dissemination of information among investors and impacts informational trading.Design/methodology/approachUsing positive volume shocks as a proxy for increased investor attention, this study evaluates the impacts of the investor-base effect and the information effect of investor attention on market correction following extreme daily returns in the US stock market from 1966 to 2018.FindingsThis study finds that the investor-base effect increases subsequent returns of both daily winner and daily loser stocks. The information effect leads to economically less significant return reversals for both the daily winner and daily loser stocks. These two effects tend to have economically more significant impacts on the daily loser stocks. The economic significance of these two effects is also related to firm size and the state of the stock market.Originality/valueThis study is the first to disentangle the investor-base effect and the information effect of increased investor attention. The evidence that the information effect facilitates the dissemination of new information and impacts stock returns contributes to the strand of studies on the impact of investor attention on market efficiency. This evidence also contributes to the strand of studies analyzing the impact of informational trading on stock returns. In addition, this study provides evidence for market overreaction and the subsequent correction. The results for up and down markets contribute to the literature on the investors' trading behavior.

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.

Investor recognition and stock returns: evidence from China

2018 ◽  
Vol 8 (2) ◽  
pp. 199-215 ◽  
Author(s):  
Hongquan Zhu ◽  
Lingling Jiang
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Capital Market ◽  
Univariate Analysis ◽  
Chinese Stock Market ◽  
Cross Sectional ◽  
Content Type ◽  
Listed Firms ◽  
Monthly Return ◽  
The Relationship

Purpose Merton’s model of capital market equilibrium under incomplete information predicts that contemporaneous stock returns are positively related to investor recognition and that future stock returns are negatively related to investor recognition. The purpose of this paper is to empirically investigate whether Merton’s theory holds true for the Chinese stock market. Design/methodology/approach This paper proposes the degree of shareholder base growth (SBG) as a proxy for investor recognition and examines the relationship between investor recognition and stock returns through a univariate analysis and Fama-Macbeth cross-sectional regressions based on A-Share listed firms. Findings The results show that investor recognition is nonlinearly and positively related to contemporaneous stock returns and is negatively related to future stock returns in contrast to the conclusions of Merton’s theory. A long-short trading strategy that involves buying stocks with the lowest SBG rate and that sells stocks with the highest SBG rate will earn an average monthly return of 3.615 percent. Research limitations/implications Though Merton’s theory is not fully reflected in the Chinese stock market, investor recognition is considered an important risk factor in the Chinese stock market. Originality/value No works have yet investigated the validity of Merton’s “investor cognition hypothesis” in relation to the Chinese stock market. This paper strives to fill this gap.

scholarly journals Stock Returns And Volatility İn An Emerging Equity Market. Evidence From Kenya

2016 ◽  
Vol 12 (4) ◽  
pp. 79 ◽  
Author(s):  
David Ndwiga ◽  
Peter W Muriu
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Time Series Data ◽  
Stock Exchange ◽  
Significant Risk ◽  
Series Data ◽  
Volatility Clustering ◽  
Cross Listing ◽  
Daily Returns

This study investigates volatility pattern of Kenyan stock market based on time series data which consists of daily closing prices of NSE Index for the period 2ndJanuary 2001 to 31st December 2014. The analysis has been done using both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. The study provides evidence for the existence of a positive and significant risk premium. Moreover, volatility shocks on daily returns at the stock market are transitory. We do not find any significant leverage effect. Introduction of the new regulations on foreign investors with a 25% minimum reserve of the issued share capital going to local investors (in 2002), introduction of live trading, cross listing in Uganda and Tanzania stock exchange (in 2006) and change in equity settlement cycle from T+4 to T+3 (in 2011) significantly reduce volatility clustering. The onset of US tapering increase the daily mean returns significantly while reducing conditional volatility.

scholarly journals Media Coverage and Sustainable Stock Returns: Evidence from China

2019 ◽  
Vol 11 (8) ◽  
pp. 2335 ◽  
Author(s):  
Tian Yang ◽  
Jinsong Liu ◽  
Qianwei Ying ◽  
Tahir Yousaf
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Media Coverage ◽  
Positive Influence ◽  
Chinese Stock Market ◽  
Investor Attention ◽  
Monthly Data ◽  
News Reports ◽  
One Year ◽  
The Relationship

This paper explores the relationship between media coverage and stock returns using monthly data of news reports from major Chinese newspapers. We find that firms with higher media coverage in the current month have higher sustainable stock returns in the following months over a one-year period compared with those with lower media coverage, which means that media coverage has a more significant and positive influence on sustainable stock returns in the markets, dominated by individual/immature investors. These results are largely robust to various robustness checks. Further empirical results demonstrate that in the Chinese stock market, a higher level of media coverage might cause higher sustained investor attention, which may drive up the buying pressure and thus lead to higher sustainable stock returns in the following year. Our results show that the effect of media coverage on stock returns depends on the characteristics of investors.

What explains the investment anomaly in the Chinese stock market?

2017 ◽  
Vol 8 (4) ◽  
pp. 495-520 ◽  
Author(s):  
Jieting Chen
Keyword(s):  
Risk Factor ◽  
Stock Market ◽  
Risk Premium ◽  
Regime Switching ◽  
Expected Returns ◽  
Chinese Stock Market ◽  
Content Type ◽  
Switching Model ◽  
Market Regimes ◽  
Markov Regime Switching Model

Purpose This paper aims to examine the Chinese investment anomaly and dissect it from a perspective of rational expectation framework. Design/methodology/approach Characteristic-based sorting and Fama–MacBeth two-stage cross-sectional regression are adopted to test the relationship between corporate investment and expected returns in both portfolio and individual stock levels. Under the framework of pricing kernels, an investment-based common risk factor is constructed to test the role of risk played in the negative investment-return relationship. Moreover, a Markov regime switching model is adopted to investigate the time-varying risk premium across market regimes. Findings Empirical results provide ample evidence showing that there is a negative relationship between investment and expected returns in the Chinese stock market. The new investment-based risk factor is found to capture the return differences across characteristic-based portfolios. In addition, risk premium of the new risk factor is not only statistically positive throughout the sample period, but also has an asymmetry that is higher during market downturn but lower under bull market. Research limitations/implications This paper merely tests the hypotheses derived from rational school. Practical implications Investment strategies based on characteristic-sorted portfolios should be adjusted to different market regimes. Originality/value First, this paper provides comprehensive empirical results by adopting different methodologies for investigating the investment anomaly in China. Second, an investment-based factor is constructed specifically for the Chinese stock market for the first time. Finally, this is the first paper to investigate the asymmetric risk premium across the Chinese bear and bull regimes by using a multivariate Markov regime switching model.

Does investor sentiment differently affect stocks in different sectors? Evidence from China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongli Niu ◽  
Yao Lu ◽  
Weiqing Wang
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Investor Sentiment ◽  
Wavelet Coherence ◽  
Chinese Stock Market ◽  
Frequency Method ◽  
Content Type ◽  
Time Frequency ◽  
Dynamic Relationship ◽  
Normal Period

PurposeThis paper aims to investigate the dynamic relationship between the investor sentiment and the return of various sectors in the Chinese stock market.Design/methodology/approachThe wavelet coherence and wavelet phase angle approaches are used to study the lead–lag associations between sentiment index and stock returns in a time–frequency way. The multiscale linear and nonlinear Granger causality tests are performed to explore whether there is a causality between them.FindingsThe empirical results show that during normal period, investor sentiment index has a stronger relationship with stock returns of industrials, consumer discretionary, health care, utilities, real estate and financial sectors. In crisis period, investor sentiment has a significant positive relationship with all industry sectors. In the short term, there is bidirectional causality between investor sentiment and stock returns of all sectors. In the medium and long run, almost all sector stock returns Granger-cause the investors' sentiment index but investor sentiment does not Granger-cause all sectors, which is in contrast to the developed markets.Practical implicationsThe interindustry impact of investment sentiment on the stock market can help construct arbitrage portfolio by investors who are interested in Chinese stock market.Originality/valueThis paper focuses on the industry sector differences of investor sentiment impact on the Chinese stock market. As far as the authors know, this is the first paper to explore the time–frequency relationship between sentiment index and industry stock returns in China using the time–frequency method based on wavelet coherence, which considers the heterogeneity of different types of investors' responses to various economic and financial events.

scholarly journals Financial Crises, Macroeconomic Variables, and Long-Run Risk: An Econometric Analysis of Stock Returns Correlations (2000 to 2019)

2021 ◽  
Vol 14 (3) ◽  
pp. 127
Author(s):  
Marco Tronzano
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Financial Crises ◽  
Stock Price ◽  
Financial Risk ◽  
Swiss Franc ◽  
Chinese Stock Market ◽  
Stock Market Index ◽  
Defensive Role ◽  
Long Run

This paper focuses on four major aggregate stock price indexes (SP 500, Stock Europe 600, Nikkei 225, Shanghai Composite) and two “safe-haven” assets (Gold, Swiss Franc), and explores their return co-movements during the last two decades. Significant contagion effects on stock markets are documented during almost all financial crises; moreover, in line with the recent literature, the defensive role of gold and the Swiss Franc in asset portfolios is highlighted. Focusing on a new set of macroeconomic and financial series, a significant impact of these variables on stock returns correlations is found, notably in the case of the world equity risk premium. Finally, long-run risks are detected in all asset portfolios including the Chinese stock market index. Overall, this empirical evidence is of interest for researchers, financial risk managers and policy makers.

Sign in / Sign up

Export Citation Format

Share Document