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The purpose of this paper is to examine the relative effects of rational and quasi-rational sentiments of individual and institutional investors on a set of smart beta fund returns. The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact). These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

The results of the impulse functions generated from a multivariate model suggest that the smart beta fund returns are negatively (positively) impacted by individual (institutional) investor sentiments.

The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact).

These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

This study creates a measure of investor sentiment directly from retail trader activity to identify misvaluation and to examine the link between sentiment and subsequent returns.

Using investor reports from a large discount brokerage that include measures of activity such as net buying, net new accounts and net new assets, this study creates a measure of retail trader sentiment using principal components. This study examines the relation between sentiment and returns through conditional mean and regression analyses.

Retail sentiment activity coincides with aggregate Google Trends search data and firms with the greatest sensitivity to retail sentiment tend to be small, young and volatile. Periods of high retail sentiment precede poor subsequent market returns. Cross-sectional results detail the strongest impact on subsequent returns within difficult to value or difficult to arbitrage firms.

This study links a rich measure of retail trader activity to subsequent market and cross-sectional returns. These results deepen our understanding of noise trader risk and aggregate investor sentiment.

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.

This paper aims to use the Covid-19 pandemic situation to conduct an experiment-like study that focuses on industry reactions under stress. Particularly, this study analyzes stock response to eight pandemic related news in 2020 across different industries. This study also investigates the role that the market risk, beta, plays in such stock reactions.

This study computes the cumulative abnormal returns (CAR) around COVID-19 events using adjusted daily stock returns of all stocks in the S&P 500 index between January 2, 2020 and December 31, 2020. This study also sorts all stocks by beta into quintiles and measures the CAR [0, +3] for each quintile around each event date.

This study finds that low beta portfolios exhibit greater abnormal returns (in absolute value) than high beta portfolios during down markets while high beta portfolios exhibit greater abnormal returns (in absolute values) when the market starts to recover. However, this study finds that beta does not seem to explain the abnormal returns reported in various industries during times of negative sentiment. During times of positive sentiment, both the beta effect and industry effect are present.

Extant literature almost unanimously concurs that the COVID-19 pandemic has brought about negative stock reactions to financial markets across the globe. Nevertheless, three interrelated issues have not been explored: market reactions during the subsequent recovery, industry heterogeneity and individual stocks’ risk profile. The study addresses these matters.

The purpose of this paper is to investigate to what extent hedge funds are subject to irrationality in their investment decisions. The authors advance the hypothesis that irrational behavior affects hedge fund returns despite their sophistication and active management style.

The irrational component may follow a pattern consistent with the observed hedge fund returns yet far distant from market fundamentals. The authors include factors beyond the original version of capital asset pricing model such as Fama and French and Carhart models, as well as less stringent models, such as APT and Fung and Hsieh, to test whether these models are able to capture the irrational nature of the residuals.

After finding that institutional irrational sentiments play a role in hedge fund returns, we note that the returns are not completely shielded against irrational trading; however, hedge fund returns appear to be affected only by the irrational component derived from institutional trading rather than that emanated from individuals.

Different sources of irrationality may have asymmetric effects on hedge fund returns. Using a different set of sophisticated investors along with different market sentiment proxies may yield different results.

The authors argue that investors can use irrational beta to gauge the extent of institutional irrational sentiments prevailing in markets for the purpose of re-adjusting their portfolios and therefore use the betas as an early warning sign. It can also guide investors in avoiding funds and strategies that display greater irrational behavior.

The study advance the idea that the unexpected, hereafter irrational, component may follow a pattern consistent with the observed hedge fund returns, yet different from market fundamentals.

Recent studies in the field of behavioral finance have highlighted the importance of investor sentiment in the return-generating process for general equities. By employing an asset pricing framework, this paper aims to evaluate the performance of European real estate equities, based on their degree of sentiment sensitivity.

Using a pan-European data set, we classify all real estate equities according to their sentiment sensitivity, which is measured relative to the Economic Sentiment Indicator (ESI) of the European Commission. Based on their individual sentiment responsiveness, we form both a high- and low-sensitivity portfolio, whose returns are included in the difference test of the liquidity-augmented asset pricing model. In this context, we analyze the performance of sentiment-sensitive and sentiment-insensitive real estate equities with a risk-adjusted perspective over the period July 1995 to June 2012.

While high-sensitivity real estate equities yield significantly higher raw returns than those with low-sensitivity, we find no evidence of risk-adjusted outperformance. This indicates that allegedly sentiment-driven return behavior is in fact merely compensation for taking higher fundamental risks. In this context, we find that sentiment-sensitive real estate equities are exposed to significantly higher market risks than sentiment-insensitive ones. Based on these findings, we conclude that a sentiment-based investment strategy, consisting of a long-position in the high-sensitivity portfolio and a short-position in the low-sensitivity one, does not generate a risk-adjusted profit.

Although this study sheds some light on investor sentiment in European real estate stock markets, further research could usefully concentrate on alternative sentiment proxies.

This is the first study to disentangle the relationship between investor sentiment and European real estate stock returns.

Although the pervasive influence of investor sentiment in equity markets is well documented, little is known about behavioral manifestations in bond markets. In this paper, we explore the impact of investor sentiment on corporate bond yield spreads. Our results reveal that bond yield spreads co‐vary with sentiment, and sentiment‐drivenmispricings and systematic reversal trends are very similar to those for stocks. Bonds appear underpriced (with high yields) during pessimistic periods and overpriced (with low yields) when optimism reigns. Consequent reversals result in predictable trends in post‐sentiment yield spreads.When beginning‐of‐period sentiment is low, subsequent yield spreads are low; high sentiment periods are followed by high spreads. High‐yield bonds (low ratings, Industrials and Utilities, extreme maturities or low durations, specially if low rated) demonstrate greater susceptibility to mispricings due to sentiment compared to low‐yield bonds. The incremental yield spread gap between highand low‐yield bonds converges subsequent to periods of low sentiment, and diverges after high sentiment. Equity attributes marginally influence the impact of sentiment on bond spreads, but mostly for distressed bonds only.

This study searches for sentimental herding in Borsa Istanbul (BIST) during the last decade using a state-space model employing cross-section standard deviations of systematic risk (Beta). It has been found that herding toward the market in the BIST-100 is both statistically significant and persistent independently from market fundamentals such as the volatility of returns and the levels of market returns. Herding trends over the sample period indicate that the financial crisis in 2000–2001 appeared to bring about sentimental herding in BIST which was followed by a calm period during which investors turned to fundamentals. Thereafter, we observe a volatile adverse herding pattern till the end of 2011 due to the confusing environment caused by the internal and external events.

This study examines the connection between investor sentiment and corporate innovation in the United States, considering the magnitude of corporate information asymmetry, the implied cost of capital and the financial constraints.

The authors employ a two-step GMM framework to examine the hypotheses of this study by utilizing annual data from 2001 to 2021 for US corporations.

The empirical evidence demonstrates a significant impact of investor sentiment on corporate innovation for firms with a lower information asymmetry and implied cost of capital than those with a higher information asymmetry and cost of capital. Although the financial constraint channel remained positive, it had little impact on the innovations of US corporations. Overall, the study's results show that companies make more valuable and high-quality patents when investors are optimistic.

This research has policy implications for all managers, investors, analysts and state officers, particularly in the USA and other developed countries. Managers and investors of all types should predict the role of corporate innovation in increasing shareholder wealth.

To the authors' knowledge, this is the first study to examine the relationship between investor sentiment and corporate innovation in the United States, considering the extent of corporate information asymmetry, the implied cost of capital and the financial limitations. The study's empirical findings uniquely contribute to the existing literature on corporate innovation and investor sentiment in the current context.