Investor sentiment, realized volatility and stock returns

As a publisher of information on capital market transactions, media coverage has become a hot topic of research for modern scholars. In this paper, stocks with trading data within 2013.1.1-2020.1.1 on the SSE and SZSE are used as research samples, and the number of media reports, investor sentiment, and stock returns are used as core variables, to investigate the transmission relationship between these three things by using a mediating effect model. The relationship between the three variables is reported systematically and directly. This study concludes that investor sentiment plays a partially mediating role in the impact of media attention on the stock market. Investor sentiment as a medium of media information translates perceptions into behavior and thus influences investors' investment decisions. The news gives investors some informational advantage reduced the level of information asymmetry and has the most pronounced impact on investors, media involvement diminishes the role of investor sentiment on stock investments.

The Sum of All Positive (Expectations) and Negative (Fears) Investor Sentiment and Stock Market Returns

Asymmetric impacts of individual investor sentiment on the time-varying risk-return relation in stock market

Sentiment stocks

Purpose: This study investigates the return co-movements associated with investor sentiment shifts in the cross-sections under a setting where market-wide sentiment interacts with short-sale impediments.
 Design/methodology/approach: This study estimates the return sensitivity to market sentiment changes (sentiment beta) for each characteristic's portfolio by regressing the return of each quintile portfolio and various high-minus-low portfolios on the sentiment changes index. It examines whether these cross-sectionally different return co-movement patterns is more prevalent during good- than bad-sentiment periods by performing the same regression separately for the good- and bad-sentiment periods.
 Findings: The result shows that the returns of speculative stocks tend to co-move more strongly with sentiment changes than those of stable stocks, in the sense that speculative stocks have higher sentiment betas than stable stocks. The cross-sectional pattern in return co-movements becomes more pronounced during the good-sentiment period but disappears during the bad-sentiment periods.
 Research limitations/implications: This study elucidates the return co-movement behavior associated with investor sentiment changes under a setting where market-wide sentiment interacts with short-sale impediments. However, analyzing the relationship between the investment sentiment index and the short-selling activities is reserved for future research.
 Originality/value: The results provide important implications for investment strategies using investor sentiment in practice, and several suggestions for establishing investor protection policies in the highly individual-crowded market. This study will contribute to enhancing the stock market efficiency and price discovery.

Purpose: Investor sentiment, the willingness of market participants to invest, is a difficult concept to measure. Exploring the relationship between investor sentiment and stock returns can reveal how investor sentiment affects the operation of the stock market. Such an understanding can assist market participants in making more rational investment decisions based on market laws. Such an understanding can also assist regulators in their roles of supervision and policy making.Methodology: Although the E-GARCH model has the advantage of considering volatility clustering, it has not previously been used to investigate the impact of investor sentiment changes on the Shanghai Composite Index's market return. This research therefore applies the E-GARCH approach to data from 2015 to 2018, to explore the influence of investor sentiment on the return rate of the Shanghai Composite Index.Main Findings: There are three main findings. First, when the investor sentiment is increased by the same amount, the rate of return before a stock market crash will have a smaller increase than the rate of change after the crash, which is a new finding. Second, the rate of return on stocks is susceptible to emotional sentiment, rather than simply depending on stock price. Third, the tendency of retail investors to follow the crowd is less in periods of pessimism than it is in periods of optimism, which, in turn, can push up stock yields.Application: Based on these research results, this article can provide insights to understand how investors' subjective judgments on future earnings affect their investment behavior and how great the impact is on the market. At the same time, it can help investors make more rational investment decisions based on an understanding of market laws, and help regulators with guidance for their supervision and policy making.Originality/Value: This paper contributes to the theory of the investor sentiment index, improving the index construction method by adding two sentiment proxy indicators: investor activity ACT and stock market leverage level. After constructing the sentiment index and comparing it with the stock market index (Shanghai Composite Index), the fit is found to be improved.

We examine the interaction effects of investor sentiment, investor crowded-trade behavior, and limited arbitrage on the cross section of stock returns. This paper presents strong evidence to reveal that investor crowded-trade behavior will increase stock prices greatly (little) among stocks with positive (negative) investor sentiment; and investor sentiment will increase (not increase) stock prices among stocks with extreme seller-initiated (buyer-initiated) crowded-trade behavior. Furthermore, this paper finds that the benchmark-adjusted returns are positive among stocks with relatively positive investor sentiment and buyer-initiated crowded-trade behavior, and negative among stocks with relatively negative investor sentiment and seller-initiated crowded-trade behavior. Finally, this paper demonstrates that limited arbitrage plays more roles on stocks with pessimistic sentiment and seller-initiated crowded-trade behavior. Taken together, this paper confirms the combined effects of investor sentiment and investor crowded-trade behavior on the cross section of stock returns, and further explores the moderating effect of limited arbitrage.

PurposeIn any stock market, volatility is a significant factor in strengthening their asset pricing. The upsurge in volatility in the stock market can activate and bring changes in the financial risk. According to financial conventional theory, the stakeholders (investors) are selected to be balanced and variations in pertinent risk are also to be anticipated due to the outcome of the drive-in basic factors in Indian stock markets. The hypothesis shows that there are actions in systematic and unsystematic risks that are determined by volatility. It is allied to sentiment-driven in the trader movement.Design/methodology/approachThe paper used the methodology of generalized autoregressive conditional heteroskedasticity-in mean GARCH-M and exponential GARCH-M (E-GARCH-M) methods on the Indian stock market. The data have been covered from 2000 to 2019.FindingsFinally, the study suggests that due to the unfitness of the capital asset pricing model (CAPM), the selection has enhanced with sentiment is an important risk factor.Practical implicationsThe investor sentiment and stock return volatility statement are established by using the investor sentiment amalgamated stock market index built.Originality/valueThe outcome of the study shows that there is an important association between stakeholder (investor) sentiment and stock return, in case of volatility behavioural finance can significantly explain the behaviour of stock returns on the Indian Stock Exchange.

The dispositional effects of holidays on investor sentiment: Therapeutic and hygienic

Effects of sentiment quantity, dispersion, and dissimilarity on online review forwarding behavior: An empirical analysis

This dissertation relates the information contained in past trading volume to investor sentiment, and investigates its ability in predicting stock returns. Investor sentiment here refers to the enthusiasm of irrational investors on an asset, relative to that of rational investors. Motivated by Baker and Stein (2004) that an increase in trading volume reflects a rise in investor sentiment, I use the change in trading volume per unit of time, referring it as the trading volume trend, as a measure of investor sentiment on individual stocks. I document a negative and significant cross-sectional relation between the trading volume trend and stock returns, both in the short term and in the long run. This relation is dynamic and holds after controlling for several liquidity measures and other possible determinants of expected returns. It also holds for various volume measures and momentum portfolios. Specifically, both winner and loser portfolios show the effect of the trading volume trend. The effect exists in stocks of small and large firms, and in optioned and non-optioned stocks. These findings suggest that the negative effect of the trading volume trend on stocks returns is robust. Moreover, a composite trading volume trend, formed on the trading volume trends of individual stocks, can predict both the equally-weighted and the value-weighted market returns in the expected direction, after controlling for other possible determinants of market returns. The composite trading volume trend also explains closed-end fund discounts. Collectively these findings support that the trading volume trend contains information on investor sentiment, and that investor sentiment has a valuation effect on stocks.

ABSTRACTIn this article, we re-examine the causality between the stock returns and investor sentiment in China. The number of net added accounts is used as a proxy for investor sentiment. To mimic the different investment horizons of market participants, we use the wavelet method to decompose stock returns and investor sentiment into time series with different frequencies. Additionally, we test for nonlinear causal relationships based on Taylor series approximation. Our results indicate that there is a one-directional linear causality from stock returns to investor sentiment on the original series, while there is a strong bi-directional nonlinear causality between stock returns and investor sentiment at different timescales.

The paper performs a (Panel) Moderated Regression, Causality and Cointegration (complete version) test on the relationship between the exchange rate of major selected cryptocurrencies vs. major fiat currencies, and selected technology indices of various geographic areas and economic groups in the world, representative of different countries or areas in the world (Europe) and for different levels of market capitalization. The dataset allows to focus on the comparison pre- and post-Covid19 (complete version), testing the relationship in those two different time frames. The novelty of the study is the use of risk aversion and VIX volatility index (market sentiment) as control variables. Various indices of risk aversion have been developed, presenting different levels of trade-off in terms of availability (ease of calculation) and effectiveness. A good candidate seems to be the Global Risk Appetite Index (GRAI) by Kumar and Persaud (2002). As for the VIX index, it has been widely used as a proxy of market sentiment, but rarely applied to the understanding of currency trends (especially cryptocurrency). Our expectation is to observe some significant moderation effect for some of the indicators considered, in general and when observing the data for different sub-periods. with investors probably trying to hedge equity investments and diversify their portfolios differently, with risk aversion and VIX index to be a driving factor of those choices. We also expect results to be confirmed by the Causality and Cointegration (complete version) test. The paper aims to contribute to the Fintech literature and shed light on the possibilities of diversification offered by tech-related portfolios. As a corollary, it aims to determine how the effect of risk aversion and general market risk sentiment drives those choices. Keywords: Cryptocurrency; Technology Index; Risk Appetite; Market Sentiment; Causality; Cointegration; Panel Regression.

Purpose This study aims to examine the effect of investor sentiment and institutional trading behavior on stock market returns and volatility in India, emphasizing asymmetric, causal and time-varying dynamics. Design/methodology/approach The study uses daily data from January 2010 to December 2024 and follows a stepwise empirical strategy. Ordinary least squares (OLS) models are employed to assess the impact of investor sentiment on returns and volatility, including asymmetries captured through positive and negative sentiment components. Pairwise Granger causality tests are used to identify directional linkages, while a term structure framework evaluates effects across different horizons. Robustness is ensured using an alternative equity index and exponential generalized autoregressive conditional heteroscedastic (EGARCH)-based volatility measures. Findings The results indicate that investor sentiment significantly affects returns and volatility, with negative sentiment exhibiting greater persistence than positive sentiment. Trading imbalances of both domestic and foreign institutional investors reduce volatility, highlighting their stabilizing roles. Granger causality tests indicate mutual feedback between sentiment and institutional activity, except for a one-way influence from domestic trading to sentiment. Term-structure analysis reveals that these effects weaken across horizons, though the influence of foreign trading remains comparatively more persistent. Practical implications The findings guide regulators in designing risk-sensitive policies and implementing timely interventions, while providing investors with evidence-based strategies for managing sentiment-driven risks. Originality/value This study provides evidence on the asymmetric and temporal effects of investor sentiment and institutional trading behavior in India's equity market, advancing behavioral finance research in emerging economies.

This paper examines the idiosyncratic volatility puzzle and whether investor sentiment influences the relation between idiosyncratic volatility and stock returns in the Chinese stock market. The findings indicate the existence of a negative idiosyncratic volatility effect. In addition, the results show that the relation between idiosyncratic volatility and returns significantly depends on investor sentiment. Thus, investor sentiment plays a very important role in reconciling the relation between idiosyncratic volatility and stock returns in the Chinese stock market. This implies that investor sentiment may be one of the major risk factors that should be considered in the Chinese stock market. In terms of predictive ability of investor sentiment, idiosyncratic volatility and market volatility, the findings indicate that idiosyncratic volatility positively predicts future excess market returns in the Chinese stock market.