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This paper aims to investigate the relations amongst investor sentiment, the structure of shareholder ownership and corporate investment.

This paper develops a theoretical model, proposes hypotheses based on the predictions of the model and conducts empirical tests. The primary method is panel regression with fixed effects. The sample covers the US data for the period between 1980 and 2018.

This paper finds that firms with a higher proportion of retail investors invest more than otherwise similar firms. In the low-sentiment periods, the financially constrained firms invest less than the non-financially constraint firms. The positive effect of residual retail ownership on the investment level is higher for firms with a higher idiosyncratic risk.

The results suggest that larger share ownership of the relatively informed institutional investors may serve as a mechanism that could reduce the degree of overinvestment caused by higher investor sentiment and the over-optimistic of the relatively uninformed investors.

This paper provides an incremental theoretical and empirical contribution to the relations amongst investor sentiment, corporate investment and the structure of shareholder ownership.

The purpose of this paper is to determine whether individual investor sentiment and its factors influence investment decision-making behavior in the Indian stock market. The study contributes to the novel conceptual framework that integrates the impact of investor sentiment and outlines the role of its factors (herding, media factor, advocate recommendation and social interaction) during the investment decision-making process.

In this paper, data were collected using a structured questionnaire survey from Indian individual investors. It uses self-reported sources of information collected via a survey of individual investors and estimated the linkage via path modeling. The collected data were analyzed using partial least square structural equation modeling to examine the relationship between the construct, namely, herding, media, advocate recommendation and social interaction with investor sentiment and investment decision-making.

The study shows that herding, media factor, advocate recommendation and social interaction significantly and positively influence the investor sentiment. Among all the factors, social interaction has the lowest influence on investor sentiment. The study also reveals that investor sentiment has a positive impact on investment decision-making.

The study provides valuable insights for the individual investors, financial advisors, policymakers and other stakeholders. Knowledge of behavioral finance would enhance the decision-making capabilities of individual investors in the stock market. Thus, the study calls for the need to increase awareness among Indian investors about behavioral finance and its usefulness in investment decision-making. The paper also sheds light upon the influence of investor sentiment and its antecedents on investment decision-making. The study confirms that the investor relies on their sentiment while making investment decisions. Hence, the stakeholders in the stock market should focus on investor sentiment and other psychological aspects of individual investors as well.

There are very few studies that deal with the behavioral aspects of individual investors in an emerging market context. The study mainly focuses on the antecedent of investor sentiment and its influence on investment decision-making in the Indian stock market. To the best of authors’ knowledge, the present study unique nature that examines the impact of the antecedent of investor sentiment which was not explored in the Indian context and investment decision-making of individual investors.

This paper aims to investigate the relationship between investors’ demographic characteristics (age, gender, education level and experience) and their investment decisions through behavioral factors (sentiment, overconfidence, overreaction and underreaction and herd behavior) as mediator variables in the Egyptian stock market.

This paper collects data from a structured questionnaire survey carried out among 384 local Egyptian, foreign, institutional and individual investors. This paper used a partial multiple regression method to analyze the effect of investors’ demographic characteristics on investment decisions through behavioral factors as the mediator variable.

Investor sentiment, overreaction and underreaction, overconfidence and herd behavior significantly affect investment decisions. Also, age, gender and the level of education have significant positive effects on investment decisions by investors. Experience does not play a significant role in investment decisions, but as investors gain experience, they tend to overlook the emotional factors.

The findings of this paper would help to understand common behavioral patterns of investors and indicate a path toward the growth of the Egyptian stock market.

There is a lack of research in behavioral finance covering Middle East and North African markets. This paper attempts to fulfill the gap by analyzing behavioral factors in the Egyptian market.

The goal of the study is to examine the effects of management discussion and analysis (MD&A) sentiment in public companies' annual reports on corporate investment incentives in developing economies.

The authors use sentiment analysis of MD&A texts based on Loughran and McDonald (2011) and combination of panel data regression, logit model and random forest. The text data consists of 3,511 annual reports of Chinese listed companies for the period from 2010 to 2019.

This paper provides empirical evidence of signaling theory that sentiment of annual reports and MD&A influences corporate decisions on both M&A and internal investments. The authors found that comparing to annual reports MD&A sentiment has more stable and significant explanatory and predictive power.

This paper confirms the importance of MD&A sentiment for corporate investment decision taking and provides practical techniques for analysts and researchers to study corporate investment incentives from the point of view of signaling theory.

The study aims to expand the domains of signaling theory and corporate investment valuation by including a broader range of data on companies' M&A and internal investments in developing economies. To explore the impact of MD&A sentiment on corporate investment, a state-of-the-art set of text mining and machine learning techniques is used. The authors' results confirm that MD&A has signaling effect and can get a positive market response. Furthermore, this study enhances the empirical evidence of overconfidence theory, i.e. optimistic management whose MD&A tend to positive overestimates the management's investments decision and also underestimate the potential risk to the firm.

The purpose of this paper is to analyze the relationship between the factors influencing investors sentiment and investment decision-making (DM) of the individual investors. This paper proposes a unique conceptual framework that incorporates the herding, market and awareness factors that are leading to investor sentiment (IS) and decision-making process of the individual investors.

This study has conducted a questionnaire-based survey to collect data from 875 individual investors through the convenience sampling method. Structural equation modeling was used to evaluate the relationship between factors, namely, market effect, herd behavior, media, social interaction and advocate recommendation that influences IS and DM.

The present study found that market effect and herding are the most significantly influencing factors of investors sentiment. Among the sources of awareness, the internet has the lowest influence when compared to media, social interaction and advocate recommendation.

This study will help individual investors to avoid the problems faced while making an investment decision. The study could help investors to select a suitable investment aid and avoid repeating expensive errors, which arise due to investors’ sentiment. It is recommended to increase the awareness regarding investors’ sentiment among individuals, so as to increase their understanding about the financial settings and to make them confident while investing. The present study also sheds light upon the behavior of Indian individual investors so that policymakers can take appropriate measures to provide the proper guidance. Policymakers can conduct awareness campaigns to increase investors’ knowledge on the market condition and to enhance proper investment DM among them.

To best of the authors’ knowledge, previous studies have focused on limited factors at a time. The present study has investigated how factors influencing investors sentiment, namely, market factors (MF), herding as well as awareness would influence investment DM among individual investors in India. The influence of these factors has never been studied simultaneously in the context of Indian individual investors’ DM.

Real estate is an asset that is traded in highly segmented, illiquid and informationally inefficient local markets. A short sale in real estate is almost infeasible and therefore impedes informed rational arbitrageurs to trade against mispricing. Thus, real estate returns are prone to sentiment-driven behaviours. Will the impacts on asset returns be identical for different types of sentiment?

This study argues that not all sentiment effects are created equal. Using the bounds test of the autoregressive distributed lag (ARDL) models, this paper examines how occupier sentiment versus investor sentiment contributes to the short-run and long-run dynamics of commercial real estate returns in Australia.

The empirical evidence suggests that investor sentiment and occupier sentiment influence return asymmetrically after macroeconomic conditions are controlled for.

The sectoral analysis further reveals that sector-specific sentiment plays a significant role in explaining commercial real estate returns. Furthermore, notable improvement is found in producing more accurate prediction in returns, given that measures of occupier and investor sentiment are appropriately specified in the forecast.

This study is novel in the sense that it acknowledges the impacts of occupiers' and investors' sentiment may be fundamentally different. The unique innovation and contribution of this study to behavioural finance literature are based on a new dataset from the Royal Institute of Chartered Surveyors which includes a survey-based measure of investor sentiment and occupier sentiment.

The purpose of this paper is to consider the role of political risk in real estate and to specifically examine the implications in Scotland of continuing uncertainty caused by political events.

The primary research links the political timeline around the Scottish independence referendum in 2014 to time series of a combination of individual investment transactions, measures of sentiment from investment agents and yields. The analysis distinguishes between UK and overseas investors.

The political risk over six years ebbed and flowed with the changing probability of constitutional change but ultimately it has been a cumulative dampener on investment in Scotland. An element of the political risk can be deemed to be specific risk linked to UK institutional fund mandates that stems from concerns about possible forced sales with independence. In addition political risk is in the eye of the beholder with overseas investors in Scotland unfazed by the prospects of independence.

The short-term impact on investment of the Scottish “neverendum” is very similar to that for independence. The consequences are depressed investment and development that seem set to continue at least until the constitutional hiatus begins to be resolved.

This is the first study to explicitly examine the impact of political uncertainty on the real estate sector.

The determinants of investor sentiment based on stock market proxies are found in numbers in empirical studies. However, investor sentiment antecedents developed from primary survey measures by constructing an investor sentiment index (ISI) are not done till date. The purpose of this paper is to fill this research gap by first developing an ISI for the Indian retail investors and then examining the investor-specific, stock market-specific, macroeconomic and policy-specific factors’ individual impact on the investor sentiment.

First, the authors develop the ISI by using the mean scores of six statements as formulated based on popular direct investor sentiment surveys undertaken throughout the world. Then, the authors employ the structural equation modeling approach on the responses of 576 respondents on 40 statements (representing the index and four study hypotheses) collected in 2016 across the country.

The results show that investor- and stock market-specific factors are the major antecedents of investor sentiment for these investors. However, interestingly macroeconomic fundamentals and policy-specific factors have no role to play in driving their sentiment to invest in the stock market.

The major implication of the results is that the Indian retail investors are showing a mixed approach of Bayesian and behavioral finance decision making. So, these implications can guide the investment consultants, regulators, other stakeholders in markets and overwhelmingly the retail investors to introspect their investment decision making across time horizons.

The formulation of ISI in an emerging market context and thereafter examining possible antecedents to influence retail investors in their investment decision making are not done till date. So, the study is unique in its research issue and findings and will have significant implication for the retail investors at least in emerging market contexts.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

This paper aims to investigate the dynamic relationship between the investor sentiment and the return of various sectors in the Chinese stock market.

The 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.

The 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.

The interindustry impact of investment sentiment on the stock market can help construct arbitrage portfolio by investors who are interested in Chinese stock market.

This 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.