Proxy For Investor Sentiment Research Articles

Investor sentiment and mutual fund flow‐performance sensitivity: Evidence from China

AbstractThis paper explores the relationship between investor sentiment and flow performance sensitivity of mutual funds in China. We first find that fund flow is positively related to performance in the past quarter and negatively associated with performance in the current quarter. Using a machine learning technique to establish proxies for investor sentiment, we show that positive sentiment mitigates this negative correlation by relieving investors' anxiety about risk and decreasing redemption. The effect is more pronounced in retail dominated funds, volatile markets and growth style funds. This finding is robust after controlling for investor attention, sentiment divergence and percentile return rankings.

Online Investor Sentiment via Machine Learning

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio.

Decrypting Metaverse crypto Market: A nonlinear analysis of investor sentiment

This study aims to investigate the role of investor sentiment in the emerging metaverse market, a novel entrepreneurship model. Empirical analyses are conducted through various causality tests to reveal the predictive power of investor sentiment on the price developments of the metaverse market. The Nonlinear Granger causality test indicates causal effects running from BTC (Bitcoin), GT (Google Trend), and FGI (Fear-Greed Index) to MVI (Metaverse Index). Further examination of these interactions through MS-VAR analysis reveals that under bear market regimes, both investor sentiment proxies (GT and FGI) and BTC have a statistically significant causal effect on the returns of MVI. This finding suggests that metaverse crypto market returns are substantially influenced by investor sentiment during periods of anxiety and turmoil, evident in steep bear markets, rather than during periods of tranquility and euphoria characteristic of bull markets. The results of the time-varying approach confirm this finding by indicating spikes in causal effects towards the end of 2021, during which a severe crash in cryptocurrency markets occurred. Overall, the causal links during market downturns may stem from the fear of missing out (FOMO) in retail investors, who mainly dominate the sentimental factors utilized in this study.

Investor sentiment or information content? A simple test for investor sentiment proxies

This study suggests a simple test to assess the validity of employing a variable as an investor sentiment proxy by examining whether the variable exerts a long-run impact on prices. We also conduct the test using news sentiment indices for the U.S. and Korea, which represent a developed and influential market and a leading emerging market, respectively, as potential proxy variables. The test results, on the one hand, lead to a pessimistic view of employing news sentiment as a proxy for investor sentiment. On the other hand, the results also demonstrate a connection between news sentiment and investor sentiment, particularly in Korea. We suggest that a variable may not be appropriate as an investor sentiment proxy even when the variable is somewhat related to sentiment.

Firm-specific investor sentiment and stock price informativeness

This paper investigates the influence of firm-specific investor sentiment on stock price informativeness in China. Using a composite firm-specific investor sentiment index, we find that sentiment positively influences stock price synchronicity and future price crash risk for opaque stocks. We use firm-specific investor sentiment proxy to develop a market sentiment proxy, which could predict aggregate price synchronicity beyond extant market sentiment indicators. Our results suggest that the composite sentiment index is a reliable proxy and investor sentiment negatively affects price informativeness.

Salience theory, investor sentiment, and commonality in sentiment: Evidence from the Chinese stock market

Investors pay more attention to a stock when the absolute return difference between the stock and the market is substantial, and consequently investors overweight this salience attribute when assessing the expected payoff from the stock. We demonstrate that the salience theory measure, which reflects this phenomenon, serves as a reliable proxy for firm-level investor sentiment and that the aggregate salience theory measure effectively represents market investor sentiment in the Chinese stock market. Furthermore, our findings reveal that the quintile portfolio with salient upsides underperforms the one with salient downsides by 1.26% (1.19% after risk adjustment) per month from 2002 to 2021. Moreover, we illustrate that the investor sentiment component of the salience theory measure significantly contributes to the negative salience premium. Finally, we provide evidence that the commonality in sentiment is priced significantly and positively, even after we control for the salience theory measure, firm risk characteristics, and lottery characteristics.

Investor sentiment and stock returns: New evidence from Chinese carbon-neutral stock markets based on multi-source data

Combining internet text and stock trading data, we construct two investor sentiment proxies for 78 carbon-neutral stocks in Chinese stock markets based on the FinBERT model and principal component analysis. We then examine the effects of the two investor sentiment indices on carbon-neutral stock returns using dynamic panel models. We also investigate the mediating role of liquidity on the effects of investor sentiment. Our results reveal that both trading and internet sentiment negatively affect stock returns through the mediating effect of liquidity. This finding holds after robustness checks for dividing the sample into pre and post-epidemics. However, due to the effects of COVID-19, carbon-neutral stock returns are found to be more susceptible to investor sentiment.

Overview of the Nexus Between Investor Sentiment-return Relation and its Antecedents: A Systematic Literature Review

Investor sentiment is the irrational belief of investors leading to over and under-reaction of stock return. Though scholarly works are growing expeditiously in this domain, there is no consensus on the impact of investor sentiment on stock return in different markets. Given the paucity of literature in terms of a summary overview of the nexus between investor sentiment and stock return, this study aims to bridge this gap by re-examining investor sentiment’s impact on stock return in the global context. A total of 108 articles were retrieved from the Scopus database spanning from 2000 to 2022. To review the articles, the study employs Scientific Procedure and Rationales for Systematic Literature Review (SPAR-4-SLR) approach. The review reveals that there exists a significant positive effect during the short run. Furthermore, trading volume, social media, initial public offerings, consumer confidence, and closed-end fund discount are found to be the most frequently used proxies for investor sentiment. The review suggests investors to exercise caution while making short-term investment decisions due to strong sentiment-return relations in the short run. The study findings help policymakers and regulators to play a vital role during abnormal market conditions such as market crashes, financial crises and pandemic situations.

Outperforming the market: a comparison of Star and NonStar analysts’ investment strategies and recommendations

AbstractWe employ StarMine to investigate the impact of analyst recommendations on stock performance. We test whether star-ranked analysts generate abnormal returns and outperform non-stars in short and long portfolios. Utilizing buy-and-hold calendar-time portfolio methodology, we calculate portfolio alphas using various asset pricing models, including CPM, the Fama and French 3-factor model, and the Carhart 4-factor model. Results indicate that all analyst groups can generate abnormal returns exceeding the market average. Star-ranked analysts outperform non-stars in short portfolios by 0.5523% in monthly alpha, though no significant difference exists in long portfolio alphas. We also conduct regressor endogeneity tests and explore investor sentiment mechanisms by utilizing the GARCH model and frequency-domain causality analysis, with NASDAQ as a proxy for investor sentiment. These tests reveal that the momentum factor is exogenous, and investor sentiments have a statistically significant positive effect on stock return volatility, with changes occurring between 5 and 10 days. This research underscores the value of analyst insights for investors, validates StarMine’s ranking effectiveness, and suggests market participants can benefit from incorporating analyst recommendations into their investment decisions. Our study makes a significant contribution to the existing literature by introducing a novel approach to understanding investor sentiment mechanisms through a causality model.

Impactos dos sentimentos racionais e irracionais dos investidores no mercado brasileiro

Abstract This article aimed to determine how rational and irrational investor sentiments impact the return and volatility of the Brazilian market. The role of sentiment in financial markets is still a recent, controversial and limited subject, and this study explores this relationship in the Brazilian market. The evidence from this research adds to the limited number of studies on investor sentiment in emerging markets and extends the evidence already documented in Brazil. Our results are expected to have an impact on investors, policymakers and monetary authorities. Investors must be aware of economic fundamentals when formulating their investment strategies and also when choosing the stocks that will make up their portfolios. Policymakers and monetary authorities should take sentiment into account when formulating political, economic and monetary strategies, as economic changes can affect investor psychology and be reflected in their stock market trading. Consumer and business confidence indexes were used as proxies for investor sentiment and were regressed against a set of economic fundamentals to isolate their rational and irrational components. Then, autoregressive vector models were estimated and impulse response functions and variance decompositions were generated to verify the impact of sentiment on the return and volatility of the Ibovespa. There are three main results. First, investor sentiment impacts both returns and volatility in the Brazilian market. Second, economic fundamentals were important determinants of returns and volatility in the Brazilian market, indicating that fundamental trading induced by rational sentiment had a greater effect than trading induced by irrational sentiment. Third, in terms of magnitude, consumer-based sentiment had a greater impact than business-based sentiment.

Сентимент и стадное поведение частных инвесторов: кластерный анализ российского фондового рынка

In this paper, the sentiment of private investors and the divergence of opinions of users of online investment platforms are analyzed as factors in the emergence of herd behavior on the stock market in different clusters. This clusters are formed on the basis of stock exchange information on shares of Russian issuers. The relevance of the study lies in determining the significance of consensus periods and their impact on the behavior of different groups of investors on the Russian stock market depending on different levels of risk taking by investors. Based on the Russian stock market data for the period from 2019 to 2023, 66 discussed Russian stocks were analyzed. To conduct sentiment analysis, an algorithm was applied to collect and automatically classify textual data using machine learning methods. As proxies for private investor sentiment, metrics of logarithmic sentiment and market-wide divergence of opinions on the stocks under discussion were constructed. The testing of the research hypotheses was based on the implementation of clustering and quantile regression analysis methods. As a result, the significant role of divergence of opinions of Internet users in the formation of herd behavior of private investors on shares with lower average return and risk level was determined. It was also found that asset clustering helps to determine the opposite behavior of investors on stocks with higher volatility level. Consensus of opinions in the market is a signal to deviate from the general market trend. Finally, the test of the hypothesis about the significant influence of investor sentiment did not provide sufficient evidence that this kind of sentiment is important for determining herd behavior in the market.

Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach

We introduce a new dimension in constructing relative investor views for the Black-Litterman model by incorporating fear/greed technical indicator predictions as a proxy for investor sentiment in the portfolio construction process. We apply a hybrid CEEMDAN-GRU deep learning model to forecast this indicator and the XGBoost ensemble learning algorithm to forecast returns for ten country ETFs and create relative views for the Black-Litterman model. These models beat several benchmark forecasting models. Our empirical results show that the proposed approach outperforms the Markowitz, minimum-variance, equally-weighted and risk-parity strategies along with four other Black-Litterman approaches from the literature for six investment periods.

Taxas de administração de fundos: o papel da concorrência da indústria e do sentimento do investidor

Abstract This article analyzes the influence of industry competition and investor sentiment on the likelihood of change in investment fund management fees in Brazil. Due to the wide variety of existing funds, with various characteristics and objectives, there may be significant differences in the fees observed. Thus, it is worth analyzing the factors that influence the amount of fund management fees charged, since the literature highlights that the payment of fees is related to fund performance. Also, it is observed that the Brazilian fund industry, despite having a large number of available funds, is still concentrated in few management firms, which is an indication that there is a low competition level. In practical terms, this investigation may be useful to investors in the fund selection process, since the management fee represents one of the main costs an investor faces when investing in this industry. The results point out the importance of adopting greater transparency in the disclosure of fees by financial institutions, since there are indications that the amounts charged are influenced by the characteristics of funds and investors. The methodology adopted involves Logit/Probit regression models, which had changes in the management fee as an explained variable and, as explanatory variables, the proxies of competition and investor sentiment, in addition to other control variables. It was observed that the investor sentiment proxy was significant in explaining the probability of change in management fees, mainly for setting higher fees. However, no statistical significance was observed for industry competition. This research innovates by analyzing the role of industry competition and investor sentiment on the probability of changing management fees, thus contributing to fill a gap found in the Brazilian national literature.

The Mediating Effect of Trading Volume on the Relationship between Investor Sentiment and the Return of Tech Companies

This paper investigates the mediation effect of trading volume to explore the relationship between investor sentiment, measured as a volatility forecast (VIX), and the return of tech companies based on the mediation analysis. This paper focuses on Tesla, a list of the 30 largest technology companies and the MSCI World Index. It implements this mediation analysis by using an ARMA-EGARCH model for the time series of Tesla stock and the MSCI World Index returns and a Fixed-Effects regression model for stock returns of the list of 30 technology companies. Estimation results show that trading volume mediates the relationship between investor sentiment and stock returns. The mediating effect found in the case of Tesla and the MSCI World Index is much more present than regarding the list of companies. Furthermore, to obtain an all-encompassing analysis and create less dependence on proxy selection, additional mediation analyses are incorporated that include the 10-year Treasury yields, prices of the Swiss franc currency and the Baker-Wurgler index as investor sentiment proxies. The results show that the mediating effect of the trading volume is present also for these proxies providing more evidence that such a mediating effect is the underlying mechanism in stock markets.

Extreme sentiment and herding: Evidence from the cryptocurrency market

Investor sentiment can be the potentially crucial factor inducing herding behavior. We advocate novel proxies for investor sentiment in cryptocurrency market and define the extreme positive (negative) sentiment as euphoria (dysphoria). We find significant herding during euphoria and dysphoria. Additionally, herding magnitude appears significantly stronger during dysphoria in most of our sample periods. Counterintuitively, the impact of extreme sentiment on herding is more pronounced in large cryptocurrencies. These findings are robust to a battery of additional tests, including alternative measurements of the market return, alternative definitions of the extreme sentiment, subperiod analysis as well as the alternative proxies for investor sentiment. Taken together, our findings indicate that extreme sentiment is an important determinant of herding in cryptocurrencies.

The impact of the COVID-19 pandemic on the nexus between the investor’s sentiment and the financial market dynamics: evidence from the Chinese market

PurposeThis study aims to investigate the impact of the COVID-19 pandemic on both of stock prices and investor's sentiment in China during the onset of the COVID-19 crisis.Design/methodology/approachIn this study, the ADCC-GARCH model was used to analyze the asymmetric volatility and the time-varying conditional correlation among the Chinese stock market, the investors' sentiment and its variation. The authors relied on Diebold and Yilmaz (2012, 2014) methodology to construct network-associated measures. Then, the wavelet coherence model was applied to explore the co-movements between these variables. To check the robustness of the study results, the authors referred to the RavenPack COVID sentiments and the Chinese VIX, as other measures of the investor's sentiment using daily data from December 2019 to December 2021.FindingsUsing the ADCC-GARCH model, a strong co-movement was found between the investor's sentiment and the Shanghai index returns during the COVID-19 pandemic. The study results provide a significant peak of connectivity between the investor's sentiment and the Chinese stock market return during the 2015–2016 and the end of 2019–2020 turmoil periods. These periods coincide, respectively, with the 2015 Chinese economy recession and the COVID-19 pandemic outbreak. Furthermore, the wavelet coherence analysis confirms the ADCC results, which revealed that the used proxies of the investor's sentiment can detect the Chinese investors' behavior especially during the health crisis.Practical implicationsThis study provides two main types of implications: on the one hand, for investors since it helps them to understand the economic outlook and accordingly design their portfolio strategy and allocate decisions to optimize their portfolios. On the other hand, for portfolios managers, who should pay attention to the volatility spillovers between investor sentiment and the Chinese stock market to predict the financial market dynamics during crises periods and hedge their portfolios.Originality/valueThis study attempted to examine the time-varying interactions between the investor's sentiment proxies and the stock market dynamics. Findings showed that the investor's sentiment is considered a prominent channel of shock spillovers during the COVID-19 crisis, which typically confirms the behavioral contagion theory.

Does online investor sentiment explain analyst recommendation changes? Evidence from an emerging market

PurposeThe authors examine how financial analysts respond to online investor sentiment when updating recommendations for specific stocks in South Africa. The aim is to establish whether online sentiment contains significant information that can influence analyst recommendations. The authors follow up the above by examining when online investor sentiment is most associated with analyst recommendation changes.Design/methodology/approachFor online investor sentiment proxies, the authors make use of the social media sentiment and news media sentiment scores provided by Bloomberg Inc. The sample size includes all companies listed on the Johannesburg Stock Exchange All Share Index. The study uses traditional ordinary least squares to examine the relation at the mean and quantile regression to identify the scope of the relationship across the distribution of the dependent variable.FindingsThe authors find evidence that pre-event news sentiment significantly influences analyst recommendation changes while no significant relationship is found with the Twitter sentiment. Further analysis shows that news sentiment is more influential when the recommendation changes are moderate (in the middle of the conditional distribution of the recommendation changes).Originality/valueThe study is the one of the first to examine the association between online sentiment and analyst recommendation changes in an emerging market using high frequency data. The authors also make a direct comparison between social media sentiment and news media sentiment, some of the most used contemporary investor sentiment proxies.

Predicting power of ticker search volume in Indian stock market

This study examines the ability of online ticker searches to serve as a valid proxy for investor sentiment and forecast stock returns and trading volumes in the Indian financial market. In contrast to the findings in prior research, we observe that ticker search volumes do not exhibit any predictive value for future excess stock returns. However, we find a weak but significant positive effect of ticker search volumes on trading volume with a two-week lag. A battery of robustness checks supports our findings. Our work warns the investors from possible misleading insights arising from search volume and stock returns related studies.

The impact of COVID-19 news and investor sentiment in European stock pricing, a regional, country, and economic sector review

In the present paper we extended the Fama-French (FF), three-factor model, by including the U.S. VIX and European VSTOXX implied volatility indexes. Also, the Baker, Bloom Davis, Kost, and Renault Economic policy (WEUI), pandemics (WPUI) news, and social media (TWITUI) uncertainty index were included. We did this in order to test if, the main Eastern, Northern, Southern, and Western European stock markets could be priced with behavioral biases such as investor sentiment. With a balanced panel data of 659 European companies from January 2017 to Jun 2021, we found that the WPUI could be used in a regional perspective, along with the VIX and TWITUI factors. In a country-specific perspective, only the inclusion of the TWITUI factor led to better-fitting results in Finland, Ireland, the Netherlands, Poland, Spain, and Sweden. Our results suggest that other pandemic news sentiment indexes should be developed and used in the main European stock markets and encourage the use of social media investor sentiment proxies in risk and asset-pricing multifactor models.

Effects of investor sentiment on stock volatility: new evidences from multi-source data in China’s green stock markets

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry. With the proposal of China's "dual carbon" target, green stocks have gradually become an essential branch of Chinese stock markets. Focusing on 106 stocks from the new energy, environmental protection, and carbon–neutral sectors, we construct two investor sentiment proxies using Internet text and stock trading data, respectively. The Internet sentiment is based on posts from Eastmoney Guba, and the trading sentiment comes from a variety of trading indicators. In addition, we divide the realized volatility into continuous and jump parts, and then investigate the effects of investor sentiment on different types of volatilities. Our empirical findings show that both sentiment indices impose significant positive impacts on realized, continuous, and jump volatilities, where trading sentiment is the main factor. We further explore the mediating effect of information asymmetry, measured by the volume-synchronized probability of informed trading (VPIN), on the path of investor sentiment affecting stock volatility. It is evidenced that investor sentiments are positively correlated with the VPIN, and they can affect volatilities through the VPIN. We then divide the total sample around the coronavirus disease 2019 (COVID-19) pandemic. The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments, especially to Internet sentiment. Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.