Cross-correlations between individual investor sentiment and Chinese stock market return: New perspective based on MF-DCCA

2018 ◽  
Vol 503 ◽  
pp. 243-256 ◽  
Author(s):  
Qingsong Ruan ◽  
Haiquan Yang ◽  
Dayong Lv ◽  
Shuhua Zhang
Keyword(s):  
Stock Market ◽  
Investor Sentiment ◽  
Chinese Stock Market ◽  
Individual Investor ◽  
Market Return ◽  
Stock Market Return ◽  
Cross Correlations ◽  
New Perspective

Chinese Stock Market Return Predictability: Adaptive Complete Subset Regressions

2016 ◽  
Vol 45 (5) ◽  
pp. 779-804 ◽  
Author(s):  
Keqi Chen ◽  
Rui Chen ◽  
Xueyong Zhang ◽  
Min Zhu
Keyword(s):  
Stock Market ◽  
Return Predictability ◽  
Chinese Stock Market ◽  
Market Return ◽  
Stock Market Return

scholarly journals The impact of political instability driven by the Tunisian revolution on the relationship between Google search queries index and financial market dynamics

2020 ◽  
Vol 4 (1) ◽  
pp. 61-76
Author(s):  
Yousra Trichilli ◽  
Mouna Boujelbène Abbes ◽  
Sabrine Zouari
Keyword(s):  
Stock Market ◽  
Political Instability ◽  
Investor Sentiment ◽  
Wavelet Coherence ◽  
Content Type ◽  
Search Queries ◽  
Market Return ◽  
Stock Market Return ◽  
Tunisian Revolution ◽  
Google Search

PurposeThis paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.Design/methodology/approachFirst, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.FindingsUsing the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.Research limitations/implicationsThis study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.Originality/valueThe important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

scholarly journals Volatility Modelling of Chinese Stock Market Monthly Return and Investor Sentiment Using Multivariate GARCH Models

2020 ◽  
Vol 5 (1) ◽  
pp. 123-133
Author(s):  
Hongjun Zeng
Keyword(s):  
Stock Market ◽  
Investor Sentiment ◽  
Multivariate Garch ◽  
Volatility Spillover ◽  
Chinese Stock Market ◽  
Volatility Spillovers ◽  
Stock Market Return ◽  
Monthly Return ◽  
Multivariate Garch Models ◽  
Linkage Effects

This article examines the linkage and volatility spillover among Chinese Stock Market Monthly Return and Investor Sentiment, investigating the effect dynamic links of various investor sentiment indicators and Chinese stock market return volatility. Employing the DCC and BEKK GARCH, we find investor sentiment is to some extent linked to the yield fluctuations of the Chinese stock market, but the volatility spillover is relatively weak. In the test period (2005-2020), we observe that several indicators do not explain their linkage effects with CSI 300 index of return fluctuations and volatility spillovers well, with no indicators can reflect both of these effects. Most indicators are linkage with the CSI 300 index, especially consumer confidence index (CCI), new investor account openings last month (NIA) and the volume of transactions last month (TURN) have significant linkage effects with the CSI 300 index. We also find that only the CCI index has a one-way volatility spillover on the CSI 300 index, and the CSI 300 index has no volatility spillover on any indicator.

scholarly journals The Nonlinear Relationship between Investor Sentiment, Stock Return, and Volatility

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Gang He ◽  
Shuzhen Zhu ◽  
Haifeng Gu
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Investor Sentiment ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Bear Market ◽  
Stock Market Return ◽  
Space Effect

Based on the DSSW model, we analyze the nonlinear impact mechanism of investor sentiment on stock return and volatility by adjusting its hypothesis in Chinese stock market. We examine the relationship between investor sentiment, stock return, and volatility by applying OLS regression and quantile regression. Our empirical results show that the effects of investor sentiment on stock market return are asymmetric. There is “Freedman effect” in Chinese stock market, but only optimistic sentiment has a significant nonlinear impact on stock market returns when the stock market is a balanced market or a bear market. Meanwhile, “create the space effect” does exist in Chinese stock market too. It only exists when the market is in equilibrium, and only pessimistic sentiment has the nonlinear effect on stock market volatility.

Volatility Modelling of Chinese Stock Market Monthly Return and Investor Sentiment Using Multivariate GARCH Models

2020 ◽  
Vol 5 (1) ◽  
pp. 123-133
Author(s):  
Hongjun Zeng
Keyword(s):  
Stock Market ◽  
Investor Sentiment ◽  
Multivariate Garch ◽  
Volatility Spillover ◽  
Chinese Stock Market ◽  
Volatility Spillovers ◽  
Stock Market Return ◽  
Monthly Return ◽  
Multivariate Garch Models ◽  
Linkage Effects

This article examines the linkage and volatility spillover among Chinese Stock Market Monthly Return and Investor Sentiment, investigating the effect dynamic links of various investor sentiment indicators and Chinese stock market return volatility. Employing the DCC and BEKK GARCH, we find investor sentiment is to some extent linked to the yield fluctuations of the Chinese stock market, but the volatility spillover is relatively weak. In the test period (2005-2020), we observe that several indicators do not explain their linkage effects with CSI 300 index of return fluctuations and volatility spillovers well, with no indicators can reflect both of these effects. Most indicators are linkage with the CSI 300 index, especially consumer confidence index (CCI), new investor account openings last month (NIA) and the volume of transactions last month (TURN) have significant linkage effects with the CSI 300 index. We also find that only the CCI index has a one-way volatility spillover on the CSI 300 index, and the CSI 300 index has no volatility spillover on any indicator.

scholarly journals Light a lamp and look at the stock market

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Radeef Chundakkadan
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
Investor Sentiment ◽  
Downside Risk ◽  
Bond Yields ◽  
Risky Assets ◽  
Market Participants ◽  
Market Return ◽  
The Impact

AbstractIn this study, we investigate the impact of the light-a-lamp event that occurred in India during the COVID-19 lockdown. This event happened across the country, and millions of people participated in it. We link this event to the stock market through investor sentiment and misattribution bias. We find a 9% hike in the market return on the post-event day. The effect is heterogeneous in terms of beta, downside risk, volatility, and financial distress. We also find an increase (decrease) in long-term bond yields (price), which together suggests that market participants demanded risky assets in the post-event day.

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