Measure Of Investor Sentiment Research Articles

Does time-varying risk aversion sentiment matter in the connectedness among Sub-Saharan African bond markets?

Several bond markets in sub-Saharan Africa (SSA) are defaulting due to hiking spreads amid the stressed states introduced by the COVID-19 pandemic and the geopolitical risk tensions from the Russia-Ukraine conflict. Are there controllable factors that drive these markets? We investigate the dynamic connection shared by SSA bond markets and assess the role of investor sentiment measures, focusing on the risk aversion sentiment of international investors. Our results, across different trading horizons, are expected to aid in the formulation of policies for regulating and developing bond markets of emerging economies, particularly SSA. In terms of both return and volatility of SSA bonds, we find risk aversion sentiment an important transmitter of spillover for all investment horizons.

Willingness to take risks for sustainability during the COVID-19 pandemic

This paper empirically shows an increase in investor attention to sustainability during the COVID-19 pandemic using various measures of investor sentiment. To understand the drivers of increased interest in sustainability among investors, we use evidence from two experiments conducted before and during the pandemic. Results collected in an investment task reveal that the shift towards sustainable investments during the pandemic is primarily driven by risk-averse individuals and those with pessimistic risk expectations. These findings suggest that investors exhibit a “willingness to take risks” for sustainability during times of crises.

Social media investors' sentiment as stock market performance predictor

PurposeThis study examines the impact of social media investor sentiment on the stock market performance through qualitative and quantitative proxies.Design/methodology/approachThe authors use a sample of daily stock performance related to S&P 500 Index for the period from December 18, 2017, to December 18, 2018. The social media investor sentiment was assessed through qualitative and quantitative proxies. For qualitative proxies, the study relies on three social media resources”: Twitter, Trump Twitter account and StockTwits. The authors proposed 3 methods to reflect investor sentiment. For quantitative proxies, the number of daily messages published from Trump Twitter account and StockTwits is considered as a signal of investor sentiment. For regression model, the study adopts the autoregressive distributed lagged to determine the relationships between the nonstationary series.Findings:Empirical findings provide evidence that quantitative measures of investor sentiment have significant effects on S&P’500 performances. The authors find that Trump's tweets should be interpreted with caution. The results also show that the number of Trump's tweets on t−1 day have a positive effect on performance on day t.Practical implicationsSocial media sentiment contains information for predicting stock returns and transaction activity. Since, the arrival of new information in capital markets triggers investor sentiment on social media.Originality/valueThis study investigates the investors’ sentiment through social media and explores quantitative and qualitative measures. The amount of information on social media reflects more the investor sentiment than content analysis measures.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0818

Will the negative psychological perceptions of investors reduce platform liquidity? Evidence from China's online loans.

Market liquidity can reflect whether financial market conditions are favorable and is the primary concern for investors when making investment decisions. Therefore, investors' psychological perception and confidence in the quality of products (assets) are particularly important. Using 264 of China's online loan platforms from August 2017 to November 2018, we investigate the impact of the negative psychological perceptions of investors on platform liquidity. The empirical results suggest that the negative psychological perceptions of investors reduce platform liquidity and increase platform liquidity risk. Using the Baidu Search Index to measure investor sentiment, we find that the negative psychological perceptions of investors affect platform liquidity by affecting investor sentiment, which provides a good channel for explaining the main conclusions. Heterogeneity analysis shows that the impact of the negative psychological perceptions of investors on platform liquidity is smaller in high-quality platforms with higher market share and higher registered capital. Meanwhile, we also find that the impact of negative psychological perceptions of investors is greater in private platforms, after the rectification policy, with positive net inflow, and in first- and second-tier cities and coastal cities. Precautionary financial regulatory policies are necessary, not punishment ex post. The research findings of this article can assist investors, platform managers, and regulatory agencies in identifying the liquidity characteristics of platforms, which can contribute to strengthening market liquidity management and financial risk control and provide some reference and support for formulating sustainable development policies in the financial industry.

The extra value of online investor sentiment measures on forecasting stock return volatility: A large-scale longitudinal evaluation based on Chinese stock market

Numerous studies have purported to show that using online investor sentiment measures can enhance the accuracy of forecasting stock return volatility. However, many of these studies did not validate their findings with out-of-sample data, had insufficient validation samples, or failed to incorporate appropriate controls in their predictive models. None of these studies compared the predictive power of online sentiment measures with commonly used technical sentiment measures, nor did they investigate whether online sentiment measures offer any additional predictive value for volatility forecasting when controlling for technical measures. Using a large stock sample in China A-shares, we contribute to examining the additional predictive power of online sentiment measures for predicting stock volatility for different forecast horizons and different time windows, while controlling for technical measures. Methodologically, through using rolling origins and moving windows evaluation framework, we consider the benefits of using global forecasting models compared to local stock-specific models, and the robustness of our appraisal of the use of sentiment measures. We found that technical sentiment measures generally perform better than online sentiments. When controlling for technical measures, online sentiment measures have extra predictive value only when the forecasting horizon is long (e.g., one week or month), but the magnitudes of the improvement are relatively limited.

Does investor's sentiment affect industries' return? – A case of selected Indian industries

PurposeIn a first of its kind, this paper tries to explore the long-run relationship between investors' sentiment and selected industries' returns over the period January 2010 to December 2021.Design/methodology/approachThe paper uses 23 market and macroeconomic proxies to measure investor sentiment. Principal component analysis has been used to create sentiment sub-indices that represent investor sentiment. The autoregressive distributed lag (ARDL) model and other sophisticated econometric techniques such as the unit root test, the cumulative sum (CUSUM) stability test, regression, etc. have been used to achieve the objectives of the study.FindingsThe authors find that there is a significant relationship between sentiment sub-indices and industries' returns over the period of study. Market and economic variables, market ratios, advance-decline ratio, high-low index, price-to-book value ratio and liquidity in the economy are some of the significant sub-indices explaining industries' returns.Research limitations/implicationsThe study has relevant implications for retail investors, policy-makers and other decision-makers in the Indian stock market. Results are helpful for the investor in improving their decision-making and identifying those sentiment sub-indices and the variables therein that are relevant in explaining the return of a particular industry.Originality/valueThe study contributes to the existing literature by exploring the relationship between sentiment and industries' returns in the Indian stock market and by identifying relevant sentiment sub-indices. Also, the study supports the investors' irrationality, which arises due to a plethora of behavioral biases as enshrined in classical finance.

Frequency connectedness between FinTech, NFT and DeFi: Considering linkages to investor sentiment

We explore the domain of FinTech, DeFi, and NFT market relationships considering investor attention. Using frequency connectedness analysis, we employ two different investor sentiment measures corresponding to cryptocurrency markets and the FinTech industry. Averaged median dynamic connectedness results indicate the dominance of the FinTech industry over the DeFi and NFT markets, with this effect more significant over short-term market cycles rather than long-term. Results of net pairwise dynamic directional connectedness analysis show increasing DeFi and NFT market stresses following the pandemic period that we identify as not related to the aftermath of the outbreak but rather to FinTech market dynamics. We also identify in turn return spillovers from DeFi, NFT, and investor sentiment for the cryptocurrency market to investor sentiment for the FinTech industry. This was especially pronounced during 2022 and potentially attributable to cyber mischief in the DeFi market. Results highlight unregulated cyber markets having the potential to engender financial instability.

Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

AbstractThis paper discusses whether investor sentiment contains useful information to predict volatility by constructing investor pessimism index based on Baidu, which is the largest search engine in China. We discover that the investor pessimism index is significantly negatively correlated with contemporaneous returns but predicts following days' returns reversals. Furthermore, the heterogeneous autoregressive model of realized volatility (HAR‐RV) with investor pessimism index performs better than other HAR‐RV‐type benchmark models. We perform a series of robustness tests, which include alternative construction of investor pessimism index, crisis subsample, alternative predictive model, alternative measures of volatility, and considering the role of institutional investor. Our results remain robust. Taken together, investor pessimism index constructed in this paper seem to be a suitable proxy to measure investor sentiment in China and can enhance the accuracy of volatility forecasting in the Chinese stock market.

An enhanced investor sentiment index*

We propose an enhancement to the well-known market-based investor sentiment index by Baker, M., and J. Wurgler. (2006. “Investor Sentiment and the Cross-Section of Stock Returns.” The Journal of Finance 61 (4): 1645–1680). The low forecasting power of that index for future aggregate stock market returns has long been a puzzle, and we demonstrate that its ability to empirically capture latent investor sentiment can be significantly improved by allowing contributions of its individual components to vary over time, rather than being fixed. Our enhanced index represents an improved measure of investor sentiment: empirically, we find that our sentiment index not only instils forecasting power to the original Baker and Wurgler (2006) index, it also outperforms competitor measures in empirically capturing unobservable sentiment. This superior empirical performance is demonstrated to be due to investors’ irrational expectations about future discount rates. Lastly, sentiment measured by our enhanced index contains useful and unique information about future market returns, as it outperforms fundamental economic predictors in forecasting, in both the statistical and economic sense.

Impact of Liquidity and Investors Sentiment on Herd Behavior in Cryptocurrency Market

This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of cryptocurrencies. This study aims to fill this research gap by examining the impact of liquidity and sentiment on herd behavior using the CSAD model, considering small, medium, and large cryptocurrencies. The results show different outcomes for cryptocurrencies of different sizes, consistently demonstrating that the herding effect is more pronounced under conditions of lower liquidity, as determined by the turnover volume and liquidity ratio of cryptocurrencies. Proxy measures such as the Twitter Hedonometer and CBOE VIX were used to measure investor sentiment and show the prevalence of herding behavior in optimistic times for all cryptocurrencies, regardless of their market capitalization. Consequently, this study provides valuable insights into the manifestation of herd behavior in the cryptocurrency market and highlights the importance of liquidity and sentiment as influencing factors. These findings improve our understanding of investor behavior and provide guidance to market participants and policymakers on how to effectively manage the risks associated with herd effects.

Overnight returns and investor sentiment: Further evidence from the Taiwan stock market

This study reexamines the relationship between overnight returns and investor sentiment—a finding documented by Aboody et al. (2018, JFQA)—on the Taiwan Stock Exchange (TWSE). We aim to verify whether the overnight returns on the TWSE are reflective of investor sentiment and clarify how the trading activities of different investor types affect the patterns of overnight returns. We show that overnight returns cause short-term persistence and long-term return reversal on the TWSE, both of which are driven by investor sentiment. In addition, the percentage of retail trading enhances the patterns of overnight returns. A high individual trading volume ratio raises the tendency of short-term overnight return persistence. Our findings provide further support for the validity of overnight returns in measuring investor sentiment. When overnight returns fail to work as a valid sentiment proxy in many non-US markets, their success on the TWSE suggests that the connection with the US market, the preference of investors for chasing information, and the structure of market participants are likely to be the most important determinants of whether they are reflective of investor sentiment.

Impact of investor sentiment on firm innovation: Evidence from textual analysis

The paper examines whether and how investor sentiment affects corporate innovation. The study finds a statistically significant relationship between investor sentiment and firm innovation. Our findings demonstrate that investor sentiment has a positive causal impact on firm innovation, as revealed through the use of the instrumental variable method and propensity score matching approach, as well as the use of changes in investor sentiment as independent variables. The results still hold after using alternative measures of investor sentiment. Our subsample analysis indicates that investor sentiment boosts innovation, regardless of R&D ratios. The estimated results after considering changes in economic conditions remain consistent with the baseline estimates. The mechanism analysis suggests that investor sentiment alleviates financing constraints and enhances R&D investment, thereby positively impacting firm innovation.

Sentiment, Herding and Volatility Forecasting: Evidence from GARCH-MIDAS Approach

Investor sentiment and herding, as two important indicators of investors’ beliefs, both have great impacts on stock market volatility. Based on the daily transaction data in the Chinese stock market, we measure investor sentiment and herding on monthly basis from 2005 to 2020. Then, two indicators are, respectively, introduced to GARCH-MIDAS model for volatility forecasting. Moreover, their forecasting abilities are compared with five GARCH-type models in four crisis periods and two up markets through the model confidence set (MCS) test. We find that herding can better explain the long-term component of volatility than investor sentiment in GARCH-MIDAS model. What is more, both investor sentiment and herding possess excellent out-of-sample forecasting performances. Finally, the forecasting ability of herding is overwhelming compared with investor sentiment in crisis periods, while investor sentiment presents better prediction accuracy than herding in up markets. Our research shows that the forecasting abilities of investor sentiment and herding on volatility may be related to the market states.

America's decoupling from China: A perspective from stock markets

AbstractAmerica's decoupling‐from‐China debate started after July 2018, reached its peak in August 2020, and is likely to continue even if it may not be a high priority for the Biden administration. Many studies have examined various aspects of this issue, especially the potential economic impacts on the US economy. Unlike previous research, this study looks at the response of stock markets. Using Google Trends data, this study created a weekly dataset from January 2020 to June 2021 to measure investor sentiment towards the US decoupling from China. Employing the generalised autoregressive conditional heteroskedasticity (GARCH) models, the study finds that concern over decoupling is associated with significant variations in stock market prices. From this we can infer that the overall effects of decoupling on the US economy are likely to be considerable.

The value premium and investors' appetite for risk

The value premium is always discussed in the literature as a phenomenon driven by rational economic factors such as risks related to leverage, default or liquidity, cash flow or technological shocks. In this study, we use parametric and non-parametric methods with daily, weekly and monthly data for 1965–2019 to show that the value premium correlates with and is predictable by investors’ appetite for risk. The latter is captured using various measures of investor sentiment, including survey-based, stock market-based, press-based, Internet search-based and social media-based proxies. In addition, utilizing the quantile regression procedure, we find that the dependence between the value premium and some of the measures of risk appetite is non-linear and varies with the distribution of the data. Our findings are based on examining 100 portfolios created based on their book-to-market ratios and size. The results hold true for different sample periods and various model specifications.

Firm-level investor sentiment and corporate announcement returns

Behavioural models of investor behaviour propose that sentiment affects investors’ responses to corporate announcements. As beliefs can be cross-sectionally heterogeneous, firm-specific investor sentiment may differ from aggregate levels of investor sentiment. We provide empirical evidence about the explanatory power of both firm- and market-level investor sentiment for corporate announcement returns. We demonstrate empirically that firm-level investor sentiment has marginal explanatory power beyond market-level investor sentiment for corporate announcement returns, turnover and long-run reversals. Previous studies, which focus on market-level investor sentiment measures are likely to have under-stated the economic magnitude of the role that sentiment plays in corporate announcement returns.

Corporate managers, price noise and the investment factor

This study investigates the impact of flows between bond and equity funds on investment factors over the period 1984–2015. It determines contemporaneous mispricing effects and a statistical reversal relation between these flows and both legs of the investment factor. The statistical reversal relationship between previous flows and the investment factor is economically significant. A one-standard-deviation shock to flows causes a 0.29% decrease in investment factor returns, which are reversed within 5 months. A trading strategy based on signals from past flows and the investment factor outperforms the market by 0.68% in the months following positive flows and produces significant alphas after accounting for well-known equity risk factors. The findings are interpreted as evidence in favor of a behavioral explanation, in which sentiment influences actual managerial decisions. When retail investors and managers are swept up in market euphoria, retail investors shift their holdings from bond to equity mutual funds, and high-investment firms invest more aggressively. Market-level euphoria has a different impact on high- and low-investment firms, and thus the investment factor can be influenced. Hence, the mispricing occurs during these periods, and the reversal relationship is especially pronounced for a high-investment portfolio versus a low-investment portfolio. As a result, during the months following periods of positive flows, the investment factor outperforms the market factor. Interestingly, this study’s measure of flows, which serves as a proxy for market-level euphoria, outperforms other measures of investor sentiment.

Investor sentiment: a retail trader activity approach

PurposeThis study creates a measure of investor sentiment directly from retail trader activity to identify misvaluation and to examine the link between sentiment and subsequent returns.Design/methodology/approachUsing investor reports from a large discount brokerage that include measures of activity such as net buying, net new accounts and net new assets, this study creates a measure of retail trader sentiment using principal components. This study examines the relation between sentiment and returns through conditional mean and regression analyses.FindingsRetail sentiment activity coincides with aggregate Google Trends search data and firms with the greatest sensitivity to retail sentiment tend to be small, young and volatile. Periods of high retail sentiment precede poor subsequent market returns. Cross-sectional results detail the strongest impact on subsequent returns within difficult to value or difficult to arbitrage firms.Originality/valueThis study links a rich measure of retail trader activity to subsequent market and cross-sectional returns. These results deepen our understanding of noise trader risk and aggregate investor sentiment.

Investor sentiment, credit rating, and stock returns

We confront the distress risk puzzle. Conventionally, stocks with high distress risk should yield higher returns. However, this notion is found to be empirically inaccurate. We develop a stock-level investor sentiment measure and find that behaviors of individual investors affect the future excess returns of stocks despite the presence of distress risk. Our findings suggest that net buying by individual investors enhances our understanding of the negative relationship between credit ratings and future stock returns. To do so, we develop a cross-sectional measure of the investor sentiment for each individual stock at each month.

The emotional cost-of-carry: Chinese investor sentiment and equity index futures basis

PurposeWhile the classic futures pricing tool works well for capital markets that are less affected by sentiment, it needs further modification in China's case as retail investors constitute a large portion of the Chinese stock market participants. Their expectations of the rate of return are prone to emotional swings. This paper, therefore, explores the role of investor sentiment in explaining futures basis changes via the channel of implied discount rates.Design/methodology/approachUsing Chinese equity market data from 2010 to 2019, the authors augment the cost-of-carry model for pricing stock index futures by incorporating the investor sentiment factor. This design allows us to estimate the basis in a better way that reflects the relationship between the underlying index price and its futures price.FindingsThe authors find strong evidence that the measure of Chinese investor sentiment drives the abnormal fluctuations in the basis of China's stock index futures. Moreover, this driving force turns out to be much less prominent for large-cap stocks, liquid contracting frequencies, regulatory loosening periods and mature markets, further verifying the sentiment argument for basis mispricing.Originality/valueThis study contributes to the literature by relying on investor sentiment measures to explain the persistent discount anomaly of index futures basis in China. This finding is of great importance for Chinese investors with the intention to implement arbitrage, hedging and speculation strategies.