Investor Sentiment Index Research Articles

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

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.

Stock Prediction Based on Optimized LSTM and GRU Models

Stock market prediction has always been an important research topic in the financial field. In the past, inventors used traditional analysis methods such as K-line diagrams to predict stock trends, but with the progress of science and technology and the development of market economy, the price trend of a stock is disturbed by various factors. The traditional analysis method is far from being able to resolve the stock price fluctuations in the hidden important information. So, the prediction accuracy is greatly reduced. In this paper, we design a new model for optimizing stock forecasting. We incorporate a range of technical indicators, including investor sentiment indicators and financial data, and perform dimension reduction on the many influencing factors of the retrieved stock price using depth learning LASSO and PCA approaches. In addition, a comparison of the performances of LSTM and GRU for stock market forecasting under various parameters was performed. Our experiments show that (1) both LSTM and GRU models can predict stock prices efficiently, not one better than the other, and (2) for the two different dimension reduction methods, both the two neural models using LASSO reflect better prediction ability than the models using PCA.

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.

Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors

This paper extracts an investor sentiment indicator for the 30 DJIA stocks based on the textual classification of 289,024 online tweets posted on the so-called StockTwits, and examines its contemporaneous and predictability effects on the dispersion of stock returns using the quantile regression technique. We find that both contemporaneous and predictability effects of sentiment are heterogeneous throughout the return distribution. Specifically, sentiment is positively contemporaneously associated with stock returns at higher quantiles. However, it is a strong negative predictor of future returns at lower quantiles. Overall, our findings are broadly consistent with most behavioural theories and show that sentiment mainly affects the valuation of assets in extreme market conditions.

Stock Prediction Based on News Text Analysis

With the vigorous development of information technology, the textual data of financial news have grown massively, and this ever-rich online news information can influence investors’ decision-making behavior, which affects the stock market. Thus, online news is an important factor affecting market volatility. Quantifying the sentiment of news media and applying it to stock-market prediction has become a popular research topic. In this study, a financial news sentiment lexicon and an auxiliary lexicon applicable to the financial field are constructed, and a sentiment index (SI) is constructed by defining the weight of semantic rules. Then, a comprehensive sentiment index (CSI) is constructed via principal component analysis of the sentiment index and structured stock-market trading data. Finally, these two sentiment indices are added to the generalized autoregressive conditional heteroscedastic (GARCH) and the Long short-term memory (LSTM) models to predict stock returns. The results indicate that the prediction results of LSTM models are better than those of GARCH models. Compared with general-purpose lexicons, the financial lexicons constructed in this study are more stable, and the inclusion of a comprehensive investor sentiment index improves the accuracy of measuring sentiment information. Thus, the proposed lexicons allow more comprehensive measurement of the effects of external sentiment factors on stock-market returns and can improve the prediction effect of stock-return models.

Quantifying Time-Frequency Co-movement Impact of COVID-19 on U.S. and China Stock Market Toward Investor Sentiment Index.

The worldwide spread of COVID-19 dramatically influences the world economic landscape. In this paper, we have quantitatively investigated the time-frequency co-movement impact of COVID-19 on U.S. and China stock market since early 2020 in terms of daily observation from National Association of Securities Dealers Automated Quotations Index (NDX), Dow Jones Industrial Average (DJIA), Standard & Poor's 500 Index (SPX), Shanghai Securities Composite Index (SSEC), Shenzhen Securities Component Index (SZI), in favor of spatiotemporal interactions over investor sentiment index, and propose to explore the divisibility and the predictability to the volatility of stock market during the development of COVID-19. We integrate evidence yielded from wavelet coherence and phase difference to suggest the responses of stock market indexes to the COVID-19 epidemic in a long-term band, which could be roughly divided into three distinguished phases, namely, 30–75, 110–150, and 220–280 business days for China, and 80–125 and 160–175 after 290 business days for the U.S. At the first phase, the reason for the extreme volatility of stock market mainly attributed to the sudden emergence of the COVID-19 epidemic due to the pessimistic expectations from investors; China and U.S. stock market shared strongly negative correlation with the growing number of COVID-19 cases. At the second phase, the revitalization of stock market shared strong simultaneous moves but exhibited opposite responses to the COVID-19 impact on China and U.S. stock market; the former retained a significant negative correlation, while the latter turned to positively correlated throughout the period. At the third phase, the progress in vaccine development and economic stimulus began to impose forces to stock market; the vulnerability to COVID-19 diminished to some extent as the investor sentiment indexes rebounded. Finally, we attempted to initially establish a coarse-grained representation to stock market indexes and investor sentiment indexes, which demonstrated the homogenous spacial distribution in the vectorgraph after normalization and quantization, implying the strong consistency when filtering the frequent small fluctuations during the evolution of the COVID-19 pandemic, which might help insights into the prediction of possible status transition in stock market performance under the public health issues, potentially performing as the quantitative references in reasonably deducing the economic influences.

Media-based investor sentiment and stock returns: a textual analysis based on newspapers

ABSTRACT This study constructs a media-based investor sentiment index based on a textual analysis of China’s leading financial newspapers. We employ both the Word2Vec technique and the dictionary method to measure the aggregate textual tone in the media news. Based on a sample of publicly listed A-share firms in China, we document that media-based sentiment is positively (negatively) related to the cross-section of stock returns over a short (long) horizon. Further analysis shows that higher information quality, measured by more analyst coverage, better audit opinions and non-governmental ownership could mitigate the effects of sentiment. Overall, our findings imply that media news contains important information for measuring the overall investor sentiment that drives the future stock price up in the short term and then down in the long term.

Investor Sentiment Index during the COVID-19 crisis: Evidence from G20 Stock Markets

The present study investigates the degree of market responses through the scope of investors’ sentiment during the COVID-19 pandemic across G20 markets, by constructing a novel positive search volume index for COVID-19. Our key findings, obtained using a Panel-GARCH model, indicate that an increased COVID19 index suggests that investors decrease their COVID-19 related crisis sentiment by escalating their Google searches for positively associated COVID-19 related keywords. Specifically, we explore the predictive power of the newly constructed index on stock returns and volatility. According to our findings, investor sentiment positively (negatively) predicts the stock return (volatility) during the COVID-19. This is the first study of its kind assessing global sentiment by proposing a novel proxy and its impacts on the G20 equity market.

Dynamic Spillover Effects of Investor Sentiment and Return between China and the United States

As the two largest economies in the world, the investor sentiment and stock return of China and the United States are the focus of global attention. In this paper, we study the dynamic spillover effects of investor sentiment and return between China and the United States. First, we use the relative price differences of 9 dual-listed companies in China and the United States simultaneously to verify whether investor sentiment affects stock returns. We find a significant positive correlation between the relative price difference of dual-listed companies and the difference of investor sentiment, indicating that the investor sentiment index indeed affects stock prices. Next, we construct the TVP-VAR model to study the dynamic spillover effects of investor sentiment and the return between China and the United States. Through the time-varying impulse response, we find investor sentiment has a significant dynamic impact on returns. Therefore, investment sentiment contagion and stock market linkage between China and the United States are obvious. In addition, we conduct various robust tests, and all results are consistent.

Using E-GARCH to Analyze the Impact of Investor Sentiment on Stock Returns Near Stock Market Crashes.

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.

Impact of COVID‐19 pandemic on risk transmission between googling investor’s sentiment, the Chinese stock and bond markets

PurposeThe main objective of this paper is to investigate whether the investors' behavior under optimistic (pessimistic) conditions has an impact on risk transmission between the Chinese stock and bond markets and the sector indices mainly during the COVID-19 pandemic.Design/methodology/approachThis study uses a new measure of the investor's sentiment based on Google trend to construct a Chinese investor's sentiment index and a quantile causal approach to examine the causal relationship between googling investor's sentiment and the Chinese stock and bond markets as well as the sector indices. On the other hand, the network connectedness is used to estimate the spillover effect on the investor's sentiment and index returns. To check the robustness of the study results, the authors employed the Chinese VIX, as another measure of the investor's sentiment using daily data from May 2019 to December 2020.FindingsIn fact, the authors found a dual causality between the investor's sentiment and the financial market indices in optimistic or pessimistic situations, which indicates that positive and negative financial market returns may have an effect on the Chinese investor's sentiment. In addition, the results indicated that a pessimistic investor's sentiment has a negative impact on the banking, healthcare and utility sectors. In fact, the study results provide a significant peak of connectivity between the investor's sentiment, the stock market and the sector indices during the 2015–2016 and 2019–2020 turmoil periods that coincide respectively with the 2015 recession of the Chinese economy and the COVID-19 pandemic.Originality/valueThis finding suggests that the Chinese googling investor's sentiment is considered as a prominent channel of shock spillovers during the coronavirus crisis, which confirms the behavioral contagion. This study also identifies the contribution of a particular interest for portfolio managers and investors, which helps them to accordingly design their portfolio strategy.

Machine Learning and Artificial Intelligence Applied in the Research of the Emotions Impact on Forecasting

According to the behavioral finance theory, the psychology and behavior of investors in capital market have an important impact on the fluctuation of stock index. Therefore, this paper assumes that there is a certain internal mechanism between investor sentiment and stock index, which can predict the overall price change of the stock market. The text mining technology and sentiment analysis method can be used to generate a total of six categorypositive and negativeinvestor sentiment time series data, with three orders; The methods such as unit root test, Granger causality test and factor analysis are used to construct the composite index of SSE investor sentiment, and then the support vector machine and neural network are adopted to predict the stock market price changes and conduct the hypothesis verification. The results indicate that the SSE investor sentiment composite index constructed by using the text data of online stock market forum and stock transaction data can improve the accuracy of stock index trend prediction, which is conducive to better decision-making of the government, online platforms, listed companies, and investment entities.

Research on Emotional Leverage Effect of Capital Flow of Well-known Investment Institutions

China’s capital market started late, compared with developed countries, China’s individual investors account for a larger proportion. Because individual investor does not have the specialized investment knowledge and the mature investment psychology, its investment behavior often will receive the external factor the interference to produce “The herd effect”, therefore, the well-known investor institutions can rely on the reputation effect brought by their own popularity to influence the stock price fluctuation by influencing the investor sentiment, thus exceeding the influence degree of the corresponding buying and selling fund flow. This paper defines the above process as the emotional leverage effect of well-known investment institutions. In this paper, we use principal component analysis to construct the index of investor sentiment, and use correlation test to find that the well-known investment institutions, by virtue of their own reputation, make their capital flow have a significant impact on investor sentiment, there is a significant positive correlation between investor sentiment and the fluctuation of individual stock prices in the industry. Small and medium-sized investors will suffer investment losses because of their blind faith in the trading strategies of well-known institutions. In order to further enhance the protection of investors’ interests, this paper puts forward suggestions to improve the protection mechanism of small and medium-sized investors’ rights and interests from the perspective of information disclosure and policy supervision.

A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news

By applying machine learning to the accurate and cost-effective classification of photos based on sentiment, we introduce a daily market-level investor sentiment index (Photo Pessimism) obtained from a large sample of news photos. Consistent with behavioral models, Photo Pessimism predicts market return reversals and trading volume. The relation is strongest among stocks with high limits to arbitrage and during periods of elevated fear. We examine whether Photo Pessimism and pessimism embedded in news text act as complements or substitutes for each other in predicting stock returns and find evidence that the two are substitutes.

Does online investor sentiment impact stock returns? Evidence from the Chinese stock market

ABSTRACT The authors use crawler software to capture the data of investor post content and reading volume from the stock bar of Eastmoney and then utilize textual analysis method to construct positive and negative investor sentiment indicators. The authors use VAR model to examine the Granger causality between investor sentiment and stock returns for the Chinese stock market. The results indicate that negative sentiment indicators have more explanatory power in determining stock returns than positive sentiment indicators. Evidence from impulse responses also shows that investor sentiment reacts to stock returns in short-term.

How does investor sentiment impact stock volatility? New evidence from Shanghai A-shares market

PurposeThe focus of the current research is to examine whether mixed-frequency investor sentiment affects stock volatility in the China A-shares stock market.Design/methodology/approachMixed-frequency sampling models are employed to find the relationship between stock market volatility and mixed-frequency investor sentiment. Principal analysis and MIDAS-GARCH model are used to calibrate the impact of investor sentiment on the large-horizon components of volatility of Shanghai composite stocks.FindingsThe results show that the volatility in Chinese stock market is positively influenced by B–W investor sentiment index, when the sentiment index encompasses weighted mixed frequencies with different horizons. In particular, the impact of mixed-frequency investor sentiment is most significantly on the large-horizon components of volatility. Moreover, it is demonstrated that mixed-frequency sampling model has better explanatory powers than exogenous regression models when accounting for the relationship between investor sentiment and stock volatility.Practical implicationsGiven the various unique features of Chinese stock market and its importance as the major representative of world emerging markets, the findings of the current paper are of particularly scholarly and practical significance by shedding lights to the applicableness GARCH-MIDAS in the focused frontiers.Originality/valueA more accurate and insightful understanding of volatility has always been one of the core scholarly pursuits since the influential structural time series modeling of Engle (1982) and the seminal work of Engle and Rangel (2008) attempting to accommodate macroeconomic factors into volatility models. However, the studies in this regard are so far relatively scarce with mixed conclusions. The current study fills such gaps with improved MIDAS-GARCH approach and new evidence from Shanghai A-share market.

Investor Sentiment and Price Discrepancies between Common and Preferred Stocks in Korea

We examine whether investor sentiment affects price discrepancies between preferred and common stocks, based on a sample of Korean firms that issue preferred stocks. While most research has focused on corporate finance features such as voting rights, we examine price discrepancies as a behavioral finance feature from a new perspective. Based on the investor sentiment index, as investor sentiment increases, price discrepancies between preferred and common stocks widen in the KOSPI market. These findings confirm that investor sentiment—not merely voting premiums, cash flow rights, and liquidity—is a significant factor in explaining price discrepancies between preferred and common stocks in Korea.

Market sentiment and the choice of payment method in mergers and acquisitions

AbstractThe study contributes to the literature that pre‐merger market sentiment plays a significant role in the choice of payment method in M&As and further impacts the market reactions to such related deals around the deal announcement date. Stock is more likely to be overvalued and chosen as the medium of exchange when the market is in the bullish periods. The higher investor sentiment prior to the deals setting under the cash payment, the better abnormal stock returns of such deals around the deal announcement date. The two commonly used sentiment indexes, BWI and CSI, are both representative for the market sentiment in relation to the choice of M&A payment methods, but the investor sentiment index, BWI, is more powerful on explaining the relationship with market reactions.

COVID-19-induced Returns, Attention, Sentiments and Social Isolation: Evidence from Dynamic Panel Model

This article develops a dynamic panel model to examine the association among coronavirus outbreak, investor attention, social isolation, investor sentiments and stock returns in the German Stock exchange. The results of the two-step GMM estimator show a significant effect of coronavirus disease 2019 (COVID-19) cases on the Frankfurt Stock Exchange after controlling for calendar anomalies, meteorological conditions, country-specific factors and oil returns. Results also show that a higher level of stock returns during social isolation (lockdown period) is explained by investor attention to buy underpriced stocks. Thus, temporary social isolation enhances an investor’s ability to make better investment decisions. Investor sentiment indicators (momentum and liquidity) are also positively associated with the stock return and partially mediate the COVID-returns link, but they have no direct effect on investor attention. The stock market attracts investor attention under good news shocks (recovered cases) when investor sentiments are optimistic. Our results are robust across the transparency level of firms and their size.

Investor Sentiment, Idiosyncratic Risk, and Stock Price Premium: Evidence From Chinese Cross-Listed Companies

We investigate whether idiosyncratic risk and investor sentiment play important roles in the price disparity between China A-shares and H-shares. To build the investor sentiment indices and decompose them into different fragments for both markets, we use both principal component analysis (PCA) and partial least squares (PLS) approaches. We further look at how idiosyncratic risk affects stock mispricing and how it deals with investor sentiment. We find that the price premium of A-shares over H-shares is strongly linked to the sentiment differential. We also discover that idiosyncratic risk has a major effect on the price premium of cross-listed companies. Moreover, a larger sentiment differential reinforces the impact of idiosyncratic risk on the price disparity. The above results remain robust after controlling for other economic factors.