Changes In Investor Sentiment Research Articles

Evolution of COVID-19 Impact on Russian Stock Market: Panic Effect

Over the past few years, many research papers have referred to stock market volatility in relation to investor attention and sentiment and our article adds to the current literature on financial market reactions to the economic consequences of COVID-19. An event such as an outbreak of an infectious disease causes a negative change in investor sentiment, which strongly influences their investment decisions and, consequently, stock market prices. The subject of the study is the mutual influence of stock market characteristics and market sentiment, during a COVID-19 pandemic crisis. The purpose of the study is to provide empirical support for the hypothesis of indirect impact of uncertainty and panic under the COVID-19 pandemic on the dynamics of the stock market in Russia. The World Health Organization and experts forecast that the world will face more than one crisis related to the spread of infectious diseases in the future, so understanding the mechanisms of mutual influence of sentiment and financial markets remains relevant. In this study, we take a novel approach to deriving an indicator for panic that has not been used before. We perform econometric modeling using a Vector Autoregressive Model (VAR) and a Vector Error Correction Model (VECM), which allows us to describe in the model not only the long-term equilibrium but also the dynamics towards it. As a result, we got consistent and efficient estimates of the long-term and short-term effects of panic and mortality rates on the volatility of the RTS stock index and found that the market reaction to COVID-19 changed as the pandemic spread: the effects of uncertainty and panic, while having a significant impact at the beginning of the crisis, faded away. The conclusions obtained in the analysis of the Russian stock market dynamics coincide with those obtained by other authors in their analysis of markets in other countries over a similar period.

The asymmetric response of sovereign credit default swaps spreads to risk aversion, investor sentiment and monetary policy shocks

This paper investigates the determinants of sovereign credit default swaps spreads in the case of European countries. More specifically, it purposes to yield insight into how sovereign credit default swaps spreads respond to changes in investor sentiment, risk aversion, and monetary policy shocks during different market states. Using monthly data from May 2009 to December 2021 and running the quantile regression model, we show that risk aversion, investor sentiment and monetary policy affect the sovereign credit risk changes, and their effects vary across countries and markets states. More importantly, Risk aversion and monetary policy display the major asymmetric role in explaining CDS premia. However, limited asymmetric explanatory power is shown for investor sentiment. Furthermore, our findings demonstrate that the recent European debt crisis has significantly shaped the sensitivity of the sovereign credit risk spreads to the studied determinants. The findings of this study are helpful for investors in forecasting sovereign European credit spreads and hedging the countries default risk.

Quantile connectedness between China’s new energy market and other key financial markets

ABSTRACT This study utilizes data from the China Securities Index for the period from September 2017 to September 2022 and employs the quantile VAR model’s connectedness measurement method to examine the static and dynamic quantile connectedness between China’s new energy sector and other financial markets. The research findings reveal a significant correlation between China’s new energy market and other financial markets. During stable market periods, China’s new energy market exhibits strong predictive power over changes in other financial markets and acts as a net transmitter of systemic return shocks. The new energy, aviation, and agriculture sectors have a substantial impact on other financial markets. Extreme shocks significantly influence spillover effects, with total connectedness levels increasing with extreme downward and upward shocks. Furthermore, in the rolling window analysis, the total risk spillover index exhibits pronounced fluctuations during extreme market conditions, indicating that changes in investor sentiment and market expectations lead to greater market volatility and more significant information transmission effects. The new energy industry demonstrates predictable risk transmission characteristics, typically acting as a net transmitter of systemic risk in both normal and extreme market conditions.

Contagion between investor sentiment and green bonds in China during the global uncertainties

This study explores the connectedness between investor sentiment (IS) and Chinese green bonds using a QVAR from 30th June 2017 to 29th June 2022. Dynamic connectedness is more apparent in the short term (23%) compared to the long term (4%). Net total directional connectedness over quantiles suggests that IS is a main net receiver of shocks during our sample period under 20% and over 80% quantile. However, IS is also a net transmitter of shocks between 20% and 80% quantile. Green bond is a net receiver of shocks over quantiles. Uncertainties such as the recent COVID-19 pandemic are attributed to changes in investor sentiment and Chinese green bonds. The findings of this article have profound implications for investors, policymakers, and the broader financial community, in terms of gaining insights into and warnings about how uncertainty occurrences can spread, and accordingly designing appropriate investment policies for stabilizing the stock market in China, and the emerging economies at large.

Economic Growth and Policy Dynamics in Contemporary China: A Comprehensive Analysis and Future Outlook

The Chinese economy has grown dramatically over the past few decades. China's economic rise is particularly intriguing because it defies conventional economic models and has been driven by unique policies and dynamics. Nevertheless, in a world characterized by increasing protectionism, international trade tensions, and changes in investor sentiment, the sustainability of China's economic growth model has come into question. Chinese policymakers should navigate these shifts as investors reconsider their positions and countries protect their domestic industries. This paper investigates the policy dynamics that have contributed to the rapid economic growth in China. It also describes the various economic models that can describe the Chinese economic model. The paper reveals s that consumption, investments, and exports are the major drivers of economic growth in China. Consumption in retail sales is robust and is projected to keep increasing yearly into the future. Keynesian theory asserts that aggregate demand is the most important driving force in an economy. As such, the aggregate demand for consumer goods by government, businesses, and households is high in China. Investments are also a major driver in creating gross fixed capital. Investments from a neoclassical perspective show that investors are concerned with maximizing The conclusion shows that in a world where countries are increasingly protective of domestic industries and also where investors are withdrawing from China, policymakers will have to focus on internal growth and consumption as the driver of economic growth.

The Impact of Investor Sentiment on Corporate Investment

With the development of the capital market and the real economy, enterprise investment is getting more and more attention; enterprise investment and the survival of the whole enterprise have a close link, with more and more phenomena presented in the market, the traditional finance in the "rational man" and "market effectiveness With more and more phenomena appearing in the market, the assumptions of "rational man" and "market effectiveness" in traditional finance are not enough to give these phenomena a reasonable explanation, and the emergence of behavioral finance relaxes the assumptions and makes the theory able to explain financial anomalies in the real market. China's capital market started late, and individual investors accounted for most factors in their investor decision-making that tend to irrational investment. This paper discusses the impact of investor sentiment changes on listed companies' investment from the perspective of behavioral finance and analyses the theoretical modeling and empirical modeling from two perspectives. Firstly, there are more individual investors in China's capital market, and it is more novel to study the investment angle of the capital market from the perspective of investor sentiment, so this study has significance to the market; secondly, it discusses the literature review of the relationship between investor sentiment and listed enterprises' investment by scholars in the past, and analyses the former's impact on the latter from theoretical modeling and puts forward hypotheses; after that, we use empirical methods to prove whether the hypotheses are valid or not; and finally analyses the results of the empirical research, the shortcomings of this paper's research and the constructive suggestions and future research space for the topics discussed in this paper.

Forecasting crude oil futures using an ensemble model including investor sentiment and attention

PurposeSince crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.Design/methodology/approachThis study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.FindingsThe authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.Research limitations/implicationsThe data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.Practical implicationsIn conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.Originality/valueThe authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.

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.

Investor Sentiment, Cross-sectional Stock Returns, and Short-Sales: Evidence From Korea

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.

The Impact of Investor Sentiment on Housing Prices and the Property Stock Index Volatility in South Africa

Abstract While prior studies have examined the predictive effect of macroeconomic and country risk components on property stock index dynamics, limited explanations exist in the literature regarding the time-varying effect of investor sentiment on housing prices. Accordingly, this study assesses the impact of investor sentiment on housing properties’ returns and the effect of investor sentiment on the conditional volatility of housing price indices under different market conditions, using GARCH, GJR-GARCH, E-GARCH and Markov-switching VAR models. We found investor sentiment to significantly impact the risk premium of the property returns, where property returns increased with positive changes in investor sentiment, and conditional volatility of property returns decreased with the same changes in investor sentiment. Investor sentiment exerts positive predictive influences on the prices of small and medium houses, in both bullish and bearish market conditions but does not affect the large housing market segment. This makes the implementation of risk-related diversification across small and medium real estate portfolios more effective than large real estate portfolios. Our findings show that investor sentiment is a plausible driver of mass investor redemption actions under conditions of uncertainty.

SENTIMENT AND INDIVIDUAL STOCK PERFORMANCE: EVIDENCE FROM CHINA

This paper investigates the effect of investor investment sentiment on individual stock returns in China. We find that investment sentiment is positively associated with stock performance contemporaneously. The Granger Causality test shows that investor sentiment is a driving force of stock price movement but not the other way around. Our constructed VAR model further suggests that the change in investor sentiment in the lagged period does not significantly affect the current stock return. In addition, both the Impulse Response and the Variance Decomposition analysis provide evidence that the stock price will increase right after a positive sentiment shock, and then follow a price drop until an intrinsic value comes up. Overall, the investor sentiment factor has a strong explanatory power to explain the variation in stock return.

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

Research in behavioral finance has shown that holidays affect investor sentiment, which in turn affects stock market returns and stock prices. These studies have focused on the relationship between holidays and stock prices and stock market returns, but little attention has been paid to how investor sentiment actually changes during holidays. This paper investigates the dispositional effects of holidays on investor sentiment using social media messages from stock market investors and natural language processing techniques. We analyze the changes in investor sentiment that occur during holidays by using trading hours as a control sample and holidays as a treatment sample. The results of the study indicate that the stock market moves from the trading session into any form of holiday period, leading to an elevation in investor sentiment, demonstrating the therapeutic effect of holidays on investor sentiment. However, the therapeutic effect of holidays on investor sentiment diminishes as the holiday proceeds and manifests a hygienic effect. The hygienic effect of holidays on investor sentiment is evidenced by the fact that they do not significantly boost investor sentiment, but they can maintain a certain level of investor sentiment. This study has important implications for people's understanding of holidays in the stock market as well as for stock market policy making.

Forecasting the equity premium with frequency-decomposed technical indicators

Practitioners widely use technical trading rules to forecast the U.S. equity premium. I decompose technical indicators into components with frequency-specific information, showing that the predictive power comes from medium-frequency variation in buy and sell signals without much evidence of predictability outside this frequency band. This pattern can be observed for common strategies based on volume, momentum, and moving average rules. In line with previous work, I find that the statistical predictability of technical indicators is centered in recessions and vanishes after sharp market rebounds. However, the out-of-sample R2 increases from less than 4% for the unadjusted indicators to more than 8% for the filtered counterparts in these periods. The medium-frequency components have a better market timing ability, and the economic gains are statistically significant. A mean–variance investor would be willing to pay a fee of 100 to 150 basis points annually to access the filtered indicators. I show that the best-performing frequencies can be identified in real time. The predictive power of medium-frequency components stems from their ability to anticipate discount rate news and changes in investor sentiment. Results are robust to different country indices and transaction costs.

Rational catering of irrational emotions: Investor sentiment and executive tone.

In China, investors generally make decisions depending on the intonation of executive announcements. A total of 20,328 observations are sampled from the Chinese equity market between 2005 and 2019. We perform principal component analysis to produce monthly sentiment indices and calculate the weighted average of the value over the fiscal year to measure the degree of investor sentiment. The results of the empirical analysis reveal that: (1) there is a significant positive correlation between market-level investor sentiment and executive tone, (2) stock price and trading-volume pressures on executives, and firm-level investor expectation and gambling tendency positively moderate the relationship between investor sentiment and executive tone, (3) executive optimism does not mediate the association between investor sentiment and executive tone, and (4) manipulated and real intonations are non-homogeneous tactics adopted by rational managers to cater to changes in investor sentiment. These findings indicate that executives' intonations are both an extension of firms' current and past performances and managers' decision-making based on sentiments and behaviors of irrational investors, which are consistent with the Catering Theory. In addition, rational executives tend to adopt various intonation tactics to respond to investor sentiment to avoid declines in stock prices and trading volumes.

The Impact of Two Kinds of Responses to COVID-19 on the Stock Prices of Multinational and Local Enterprises

<p>Ever since COVID-19 became popular around the world, there are two kinds of measures taken by governments to respond to the impact on stock markets, including controlling the number of new cases and promulgating economic policies to stimulus economy. This paper studies how the two measures, controlling the spread of COVID-19 and formulating economic policies, impact stock returns in United States and what are the differences. Additionally, this paper analyses if these two measures have the same impact on multinational companies and local companies. By using CAPM and descriptive statistical analysis, the researchers find that economic stimulus is effective in short term but controlling the spread of pandemic plays the key part in long-term economic growth, international trades make a difference in reducing risk during COVID-19. Finally, to understand is the impact of investor sentiment on stock prices the reason for these two responses to changes in stock returns, Fama-French three factor with sentiment factor added is used to explore the relationship between investor sentiment and stock returns. The conclusion is that changes in investor sentiment are positively correlated with the excess return of the portfolio.</p>

The Spillover Effect and Dynamic Correlation of the China-US Bean Futures Markets Based on Investor Sentiment

This study analyzes the spillover effect and dynamic correlation of the China-US bean futures markets and discusses the relationship between the dynamic correlation of the bean futures price index and investor sentiment. First, the spillover effect of the China-US bean futures markets is analyzed through the BEKK-GARCH model. Then, the DCC-GARCH model is used for obtaining the dynamic correlation coefficients of the China-US bean futures markets. Next, the principal component analysis method is chosen to construct a comprehensive investor sentiment index. Lastly, the dynamic impacts between the change in investor sentiment and the correlation of the China-US bean futures price index are discussed through the ensemble empirical mode decomposition and impulse response analysis. The results show that the spillover effect of different degrees and directions exists between the China-US bean futures markets, and the dynamic correlation coefficients among different bean futures are also different. Besides, a certain degree of interactions exists between the high-frequency and low-frequency components of the comprehensive investor sentiment index and the dynamic correlations of bean futures price indexes.

Sentiment changes and the Monday effect

We examine whether investor sentiment changes explain the decrease in stock prices after weekends, also known as the Monday effect. Absolute changes in sentiment significantly and positively impact stock returns, whereas its positive impact declines after weekends. The negative stock returns on Mondays disappear after controlling for the impact of over-the-weekend sentiment changes under the pessimistic sentiment.

The Quant Cycle

Traditional business cycle indicators do not capture much of the large cyclical variation in factor returns. Major turning points of factors seem to be caused by abrupt changes in investor sentiment instead. The author infers a quant cycle directly from factor returns, which consists of a normal stage that is interrupted by occasional drawdowns of the value factor and subsequent reversals. Value factor drawdowns can occur in bullish environments due to growth rallies and in bearish environments due to crashes of value stocks. For the reversals, the author also distinguishes between bullish and bearish subvariants. Empirically, he shows that his simple three-stage model captures a considerable amount of time variation in factor returns. The author concludes that investors should focus on better understanding the quant cycle as implied by factors themselves, rather than adhering to traditional frameworks that, at best, have a weak relation with actual factor returns.

Share pledge transactions as an investor sentiment indicator - Evidence from China

This paper examines how share pledge transactions signal changes in investor sentiment. Based on the investor sentiment model proposed by Gervais and Odean (2001), we develop an investor sentiment model by incorporating the impact of share pledge transactions on stock prices by linking the signal transmission of the share pledges with investor decision making. Using data from the China stock market from 2014 to 2018, we find that share pledge transactions can lead to emotional bias and consequently affect investment decisions. Specifically, stock market volatility increases with changes in both optimistic and pessimistic investor sentiment bias regarding share pledge transactions. From the perspective of behavioral finance, with the Vector Autoregression (VAR) model and the impulse response test, we study the relationship between investor sentiment indicators, including share pledge factors and stock market volatility. Among the investor sentiment indicator components, we find that the composition weights of the share pledge volume and the share pledge ratio are 22.54 %, and 21.49 % respectively. Share pledge transactions have an obvious impact on investor sentiment. The paper also shows that share pledge transactions have an accelerating effect on stock price cyclical fluctuations through the investor sentiment effect. During a stock index crash, such transactions may constitute a potential market factor of instability.

Assessing the reversal of investor sentiment

Assessing the reversal of sentiment in stock markets is needed because, according to the social mood cycle, the change of social mood over time is an antecedent of price movements. The purpose of this study is to empirically assess reversal of investor sentiment, to show the phases of social mood cycle from increasing mood to decreasing mood, and to explain the dynamic change in market inefficiency from increasing to decreasing. Growth modeling, developed particularly for dealing with the change over time, is used in this study for assessing the reversal of investor sentiment. The autocovariance structure of errors and the variances/covariances of the random coefficients are all taken into account in the model. The results have indicated that the change in investor sentiment over time is inverted U-shaped for the entire market. Moreover, arbitrage constraint and stock characteristics exert a joint moderating effect on sentiment reversal. Less arbitrage constraint can strengthen sentiment reversal only when the market for individual stocks is dominated by noise traders. Based on the results obtained, we discuss asset pricing, liquidity management, and market intervention.