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This paper aims to examine the effects of investor sentiment on the development of the real estate corporate bond market in Vietnam.

The research uses an autoregressive distributed lag (ARDL) model with quarterly data. Additionally, the study employs Google Trends search data (GVSI) related to topics such as “Real Estate” and “Corporate Bond” to construct a sentiment index.

The empirical outcomes reveal that real estate market sentiment improves the growth of the real estate corporate bond market, while stock market sentiment reduces it. Also, there is evidence of a long-run negative effect of corporate bond market sentiment on the total value of real estate bond issuance. Further empirical research evidences the short-term effect of sentiment and economic factors on corporate bond development in the real estate industry.

Due to difficulties in collecting data, this paper has the limited sample of 54 valid quarterly observations. Moreover, the sentiment index based on Google search volume data only reflects the interest level of investors, not their attitudes.

These results yield important implications for policymakers in respect of strengthening the corporate bond market platform and maintaining stability in macroeconomic and monetary policies in order to promote efficient and sustainable market development.

The study offers some suggestions for regulators and governments to improve the real estate corporate bond market.

This is the first quantitative study to examine the effect of sentiment factors on real estate corporate bond development in Vietnam.

The purpose of this study is to determine which quantitative metrics are most representative of investor sentiment in the US equity markets. Sentiment is the aggregation of consumers', investors', and producers' thoughts and opinions about the future of the financial markets. By analyzing the change in popular economic indicators, financial market statistics, and sentiment reports, we can gain information on investor reactions. Furthermore, we will use machine learning techniques to develop predictive models that will attempt to forecast whether the stock market will go up or down based on the percent change in these indicators.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index.

The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets.

The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices.

The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns.

The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high.

The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

Behavioral finance and market microstructure studies suggest that the investor sentiment and liquidity are related. This paper aims to examine the aggregate sentiment–liquidity relationship in emerging markets (EMs) for both the sample period and crisis period. Then, it verifies this relationship, using the asymmetric sentiment.

This study uses a sample consisting of stocks listed on the SSE Shanghai composite index (348 stocks), the JKSE (118 stocks), the IPC (14 stocks), the RTS (12 stocks), the WSE (106 stocks) and FTSE/JSE Africa (76 stocks). This is for the period ranging from February, 2002 until March, 2021 (230 monthly observations). We use the panel data and apply generalized method-of-moments (GMM) of dynamic panel estimators.

The empirical analysis shows the following results: first, it demonstrates a significant relationship between the aggregate investor sentiment and the stock market liquidity for the sample period and crisis one. Second, referring to the asymmetric sentiment, we have empirically given proof that the market is significantly more liquid in times of the optimistic sentiment than it is in times of the pessimistic sentiment. Third, using panel causality tests, we document a unidirectional causality between the investor sentiment and liquidity in a direct manner through the noise traders and the irrational market makers and also a bidirectional causality in an indirect channel.

The results reported in this paper have implications for regulators and investors in EMs. Firstly, the study informs the regulators that the increases and decreases in the stock market liquidity are related to the investor sentiment, not financial shocks. We empirically evince that the traded value is higher in the crisis. Secondly, we inform insider traders and rational market makers that the persistence of increases in the trading activity in both quiet and turbulent times is associated with investor participants such as noise traders and irrational market makers.

The originality of this work lies in employing the asymmetric sentiment (optimistic/pessimistic) in order to denote the sentiment–liquidity relationship in EMs for the sample period and the 2007–2008 subprime crisis.

This study aims to investigate whether and how different sentiments affect the stock market’s reaction to the American Customer Satisfaction Index (ACSI) information.

The portfolio approach, with time-varying risk factor loadings and the asset-pricing models, is borrowed from the finance literature to investigate the ACSI-performance relationship. A direct sentiment index is used to examine how investors’ optimistic, neutral and pessimistic sentiments affect the aforementioned relation.

This paper finds that customer satisfaction is a valuable intangible asset that generates positive abnormal returns. On average, investing in the Strong-ACSI Portfolio is superior to investing in the market index. Even when the stock market holds pessimistic beliefs, investors can beat the market by investing in firms that score well on customer satisfaction. The out-performance of our zero-cost, long–short ACSI strategy also confirms the mispricing of ACSI information in pessimistic periods.

Findings are limited to firms covered by the ACSI data.

Finance research has further documented evidence of the stock market under-reacting to intangible information. For example, firms with higher research and development expenditures, advertising, patent citations and employee satisfaction all earn superior returns. Literature also proves that investors efficiently react to tangible information, whereas they undervalue intangible information. In summary, combining our results and those reported in the literature, customer satisfaction is value-relevant for both investors and firm management, particularly in pessimistic periods.

This study is the first to investigate how sentiment affects the positive ACSI-performance relationship, while considering the time-varying property of risk factors. This study is also the first to show that ACSI plays a more important role during pessimistic periods. This study contributes to the growing literature on the marketing–finance interface by providing better understanding of how investor emotional states affect their perceptions and valuations of customer satisfaction.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

The purpose of this paper is to examine the role of online freelance stock analysts in correcting mispricing of hard-to-value firms during sentiment-driven market periods.

The sample covers 23,758 Seeking Alpha articles obtained for the period between January 2005 and September 2011. The authors use OLS regressions to test the stock market reaction around Seeking Alpha analysts’ reports. The information in online analysts’ reports is measured by the tone of stock articles posted in SeekingAlpha.com (SA).

The analysis reveals that the degree of negative tone of their stock articles is related to three-day stock returns around the article posting dates. It further reveals that the relation between these returns and prevailing market sentiment depends on firm-specific susceptibility to the market sentiment. The three-day stock returns are higher during low market sentiment periods for firms that are more susceptible to the market sentiment, hence, harder to value. The tone of the stock articles during low sentiment periods also predicts the news in the forthcoming earnings.

The findings help stock investors identify value-relevant information provided by online freelance stock analysts, particularly for hard-to-value stocks and during the low market sentiment period.

This study utilizes a unique dataset obtained from SA. This is the first paper to examine whether online analysts help investors correct potential undervaluation of hard-to-value firms during the low market sentiment period.

The purpose of this paper is to review and discuss the literature focusing on defining and measuring sentiments so as to understand their role in stock market behavior.

Critical review of the literature by analyzing myriad scholarly articles. The study is based on an analysis of 81 scholarly articles to critically analyze the approach toward defining and measuring market sentiments. The articles have been examined to identify and critique different classification of sentiment measures. A discussion is built to scrutinize the sentiment measures under the purview of theoretical underpinnings of the investor sentiment theory as well.

With more than five decades of research, the sentiment construct in finance literature is still ill-defined. Myriad empirical proxies of sentiment measures have led to conflicting results. The sentiment construct defined in financial theories needs to be revisited from the lens of sentiments defined in psychology.

The study is limited to analyzing the role of individual and institutional sentiments in equity markets. There is a need to explore sentiments with respect to different investment styles and strategies along with the type of investors.

Developing a suitable sentiment proxy can result in devising profitable trading strategies for investors. Understanding factors driving investor sentiments will help regulators to become more proactive and frame better policies.

This paper has leveraged psychology literature to highlight the limitations in development of sentiment construct in finance literature. By identifying stylized facts from reviewing the empirical literature, it highlights areas for future research.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.