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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.

Informal migrant workers and street vendors have long been recognized as vulnerable groups in urban areas of Global South countries. However, limited studies exist on the economic challenges faced by migrant street vendors during crises. We aim to address this gap by shedding light on their livelihood and welfare losses during a public health crisis.

This research uses descriptive and qualitative analyzes to triangulate the results. Data are derived from surveys and in-depth interviews with migrant street vendors in the two biggest cities in Vietnam during the COVID-19 pandemic.

The street vendors experienced significant business loss and consumption reduction during social distancing as well as encountered difficulties in recovering their businesses in the “new normal.” These adverse consequences were also found to disproportionately affect women vendors. Additionally, despite adopting various strategies and mitigation mechanisms to sustain their businesses and consumption, these efforts proved insufficient.

This research underscores the importance of short-term and long-term urban policies aimed at supporting and promoting the social inclusion of street vendors, particularly migrant and women vendors.

This research represents one of the early attempts to explore the adverse effects of a public health crisis on migrant street vendors and to examine whether the crisis disproportionately affected vendors from different genders and educational backgrounds. It also examines their business recovery in the “new normal.”

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.

This research aspires to contribute in the area of exploration of the psychological traits evolving by practitioners within the project management profession. Specifically, it investigates whether there is any difference in optimism levels among experienced project management practitioners and newcomers in the profession.

The research used the life orientation test-revised (LOTR) (Scheier et al., 1994) to calculate respondents’ optimism scores. With these scores at hand, the researchers could then apply inferential statistics in order to deduce any differences observed among optimism score and the respondents’ characteristics (age, years of experience etc.).

Based on the results of this research, several demographic variables were shown to be statistically significant with optimism. These were (1) the number of years of experience the respondent had in managing projects, (2) working in a government organisation and (3) possessing specific project management certifications, all of which were found to adversely affect the respondent’s optimism score.

This research was unique in applying a well-known psychological test instrument (LOTR) to provide insight into the psychological impacts of a career as an information technology (IT) project manager. It is also highly likely that this correlation between the length of time working as a project manager and the adverse impact on their optimism would also apply to not just IT project managers but all experienced project managers.

This paper addresses a gap in task performance research, with a focus on supply chain operations, by exploring the role that defensive pessimism (DP)—a phenomenon sparsely studied in supply chain literature—has in the workplace. It investigates the roles that task complexity, perceptions of control and employee situatedness in the workplace play as predictors of DP, as well as addresses the relationship between defensive pessimism and supply chain performance.

Five hypotheses are developed and empirically tested employing the data-generating method, Monte Carlo simulation and then applying factor analysis and structural equation modeling (SEM) to survey data from practitioner members of the Council of Supply Chain Management Professionals.

The results reveal that task complexity and external locus of control heighten perceptions among employees that task completion could be outside their locus of control. The increased tendency to be defensively pessimistic about workplace commitments negatively impacts supply chain performance. This study found that task complexity and external locus of control encourage DP, negatively impacting supply chain performance (SCP).

This study explored underlying causes of defensive pessimism, a self-limiting behavior among supply chain professionals. In understanding the role of DP, it is possible to enhance SCP by managing task complexity, external locus of control and job autonomy—predictors of defensive pessimism in work commitments.

Aim of this study is to examine how environmental concern and perceived consumer effectiveness (PCE) mediate the effect of consumer optimism and pessimism on Generation Z's intention to purchase sustainable clothes.

A survey was administered to 247 Gen Z students using a quantitative methodology. Structural equation modeling was used for hypothesis testing.

The examination of findings provides support for the idea that both optimism and pessimism have a direct impact on environmental concerns and perceived consumer effectiveness. Additionally, it reveals that environmental concerns and perceived consumer effectiveness play a positive role in influencing sustainable clothing purchases.

This study is one of the first marketing studies to explore the relationship between personality traits, environmental concerns and the intention to purchase sustainable clothing, providing insight into their relationship.

Some methodological limitations impact the contributions of this cross-sectional investigation. It only tested a few variables predicting the intention to purchase sustainable clothing.

This research provides decision-makers, including marketers, with insights on leveraging dispositional traits to increase consumers' purchase intention of sustainable clothing.

Environmental concerns and PCE have a positive effect on sustainable clothing purchases, which are influenced by both optimism and pessimism.

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.

Emotions, understood as evolving mental states, are pivotal in shaping individuals“' decision-making, especially in ambiguous information evaluation, probability estimation of events, and causality analysis. Public–private partnership (PPP) projects represent a confluence of “economic–environmental–social” dimensions, wherein stakeholder behavior follows the sequential progression of “cognition–emotion–action.” Consequently, comprehending the effects of emotional shifts on stakeholder's decision-making processes is vital to fostering the sustainability of PPP projects.

The paper utilizes rank-dependent expected utility and evolutionary game theory to systematically examine the influence of emotional factors on stakeholders' behavior and decision-making processes within PPP projects. The paper integrates three emotional state functions—optimism, pessimism and rationality—into the PPP framework, highlighting the intricate interactions among the government, private sector, surrounding public and the media. Furthermore, the paper amalgamates the evolutionary pathways of environmental rights incidents with the media's role. Through equilibrium analysis and numerical simulation, the paper delves into the diverse interplay of emotions across different phases of the environmental rights incident, assessing the impact of these emotions on the evolutionary game's equilibrium results.

Emotions significantly influence the microlevel decisions of PPP stakeholders, adapting continually based on event dynamics and media influences. When the private sector demonstrates optimism and the surrounding public leans toward rationality or pessimism, the likelihood of the private sector engaging in speculative behavior escalates, while the surrounding public refrains from adopting a supervisory strategy. Conversely, when the private sector is pessimistic and the public is optimistic, the system fails to evolve a stable strategy. However, when government regulation intensifies, the private sector opts for a nonspeculative strategy, and the surrounding public adopts a supervisory strategy. Under these conditions, the system attains a relatively optimal state of equilibrium.

The paper develops a game model to examine the evolutionary dynamics between the surrounding public and private sectors concerning environmental rights protection in waste incineration PPP projects. It illuminates the nature of the conflicting interests among project participants, delves into the impact of emotional factors on their decision-making processes and offers crucial perspectives for the governance of such partnerships. Furthermore, this paper provides substantive recommendations for emotional oversight to enhance governance efficacy.

This paper aims to identify the impacts of sociodemographic covariates on behavioural biases (BB) scores; the psychometric evidence of the BB measurement instruments; and the main BB that influences the decision-making of individual investors.

Papers were retrieved through search using keywords in ten databases. This systematic review is based on 69 peer-reviewed papers, most of which were published between 2017 and 2021. The relevance of the included papers was assessed through the analysis of statistical/psychometric methods used, and content analysis of the BB literature and its sociodemographic correlations.

Overconfidence is higher in men and not related to age. There was no consensus regarding the relationship between BB and other sociodemographic variables. Most measuring instruments are ad hoc, showing ≤ 4 types of psychometric evidence and assessing ≤ 9 BB. Therefore, the findings demonstrate that there is no gold standard instrument for measuring investors’ BB. Furthermore, 37 BB were cited as influencers of individual investors’ decision-making and overconfidence, herding, anchoring, representativeness and loss aversion were the most prevalent.

Considering that very few systematic reviews have been published in the behavioural finance area, this paper highlights the current state-of-the-art and identifies significant gaps in the literature that can be explored by further research.

To the best of the authors’ knowledge, this is the first systematic review that analyses the psychometric properties of instruments used for individual investors BB assessment.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.