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The paper argues for the introduction of pedagogies of optimism (and implicitly of hope) in schools as a response to the danger of pessimism, skepticism and helplessness characterizing periods of armed conflicts.

A conceptual analysis.

Following an analysis of armed conflicts and their impact on educational systems, the critical contribution of pedagogies of optimism is illuminated. Teachers are encouraged to follow major phases in this kind of pedagogy, such as discovery, acceptance of the situation, imagination and critical thinking.

Practical suggestions are provided at the end of the paper.

The relevance of transparency related to public finances is considered fundamental for good economic policy management. An environment of greater fiscal transparency allows the private sector greater predictability, improving the entrepreneur’s decision-making ability. This study empirically analyzes fiscal opacity’s effect on business confidence in an emerging economy.

We use monthly data from the Brazilian economy from January 2010 to March 2023. Based on Ordinary Least Squares (OLS) and the Generalized Method of Moments (GMM) regressions, we analyze whether fiscal opacity, measured by the signal-to-noise ratio, affects business confidence. Moreover, to evaluate the duration of a shock transmitted by the fiscal opacity on business confidence, we consider an impulse-response function generated by a Vector Auto-Regressive (VAR).

We found that fiscal opacity resulting from the lack of information to anticipate the budgetary result of the public sector deteriorates business confidence.

We present robust empirical evidence that allows us to assume that using a strategy to reduce fiscal opacity through mechanisms that provide reliable economic data and fiscal forecasts is essential for fiscal policy to affect business confidence positively. Reducing fiscal opacity provides greater clarity regarding the budget outcome, reduces economic uncertainty and improves the fiscal policy expectation channel.

This paper is the first to analyze how the lack of information for market agents to anticipate the government’s budget execution accurately (fiscal opacity) affects business confidence.

The purpose of our study is to study the impact of chief executive officer (CEO) optimism and narcissism on the company's capital structure.

(1) The authors opt for regression, machine learning and text analysis to explore the impact of narcissism and optimism on the capital structure. (2) We analyze CEO interviews and employ three methods to evaluate narcissism: the dictionary proposed by Anglin, which enabled us to assess the following components: authority, superiority, vanity and exhibitionism; count of first-person singular and plural pronouns and count of CEO photos displayed. Following this approach, we were able to make a more thorough assessment of corporate narcissism. (3) Latent Dirichlet allocation (LDA) technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs and to find differences between the topics of interviews and letters provided by narcissistic and non-narcissistic CEOs.

Our research demonstrates that narcissism has a slight and nonlinear impact on capital structure. However, our findings suggest that there is an impact of pessimism and uncertainty under pandemic conditions when managers predicted doom and completely changed their strategies. We applied various approaches to estimate the gender distribution of CEOs and found that the median values of optimism and narcissism do not depend on sex. Using LDA, we examined the content and key topics of CEO interviews, defined as positive and negative. There are some differences in the topics: narcissistic CEOs are more likely to speak about long-term goals, projects and problems; they often talk about their brand and business processes.

First, we examine the COVID-19 pandemic period and evaluate how CEO optimism and pessimism affect their financial decisions under specific external conditions. The pandemic forced companies to shift the way they worked: either to switch to the remote work model or to interrupt operations; to lose or, on the contrary, attract clients. In addition, during this period, corporate management can have a different outlook on their company’s financial performance and goals. The LDA technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs. Second, we use three methods to evaluate narcissism. Third, the research is based on a set of advanced methods: machine learning techniques (random forest to reveal a nonlinear impact of CEO optimism and narcissism on capital structure).

The purpose of this study is to analyze how sentiment affects economic activity in Brazil.

Based on a nonlinear autoregressive distributed lag (NARDL) model, this study examines in detail the short-term and long-term asymmetric impacts between the variables during the period from January 2007 to December 2020.

There are three main results of this study. First, sentiment is an important factor for economic activity in Brazil, and its effect possibly occurs through the channels of consumption and investment, which are the two main components of economic growth. Second, sentiment affects economic activity in different ways in the short and the long term: in Brazil, although in the short-term, immediate shocks of sentiment may be confusing, the negative shocks from previous periods have a negative impact on economic activity. Third, the effect of shocks of optimism and pessimism on economic activity is asymmetric, and in the long run, only shocks of optimism have a significant and positive impact.

The relationship between sentiment and economic activity is still a controversial issue in the literature and this study seeks to advance its understanding in Brazil.

In this paper, we provide new evidence to strengthen the stock market overreaction hypothesis by examining a new context that has not been explored before. Our research is inspired by the widely held belief that investor sentiment experiences abrupt changes from optimism to pessimism as the market switches between bull and bear states.

If the stock market overreaction hypothesis is correct, it implies that investors are inclined to become excessively optimistic during bull markets and overly pessimistic during bear markets, resulting in overreaction and subsequent market correction. Consequently, the study first develops two testable hypotheses that can be used to uncover the presence of stock market overreaction with subsequent correction. These hypotheses are then tested using long-term data from the US market.

The study's findings support the hypothesis while also revealing a significant asymmetry in investor overreaction between bull and bear markets. Specifically, our results indicate that investors tend to overreact towards the end of a bear market, and the subsequent bull market starts with a prompt and robust correction. Conversely, investors appear to overreact only towards the end of a prolonged bull market. The correction during a bear market is not confined to its initial phase but extends across its entire duration.

Our study has some limitations related to its focus on investigating stock market overreaction in the US market and analyzing the pattern of mean returns during bull and bear market states. Expanding our study to different global markets would be necessary to understand whether the same stock market overreaction effect exists universally. Furthermore, exploring the relationship between volatility and overreaction during different market phases would be an exciting direction for future research, as it could provide a more complete picture of market dynamics.

Our study confirms the presence of the stock market overreaction effect, which contradicts the efficient market hypothesis. We have observed specific price patterns during bull and bear markets that investors can potentially exploit. However, successfully capitalizing on these patterns depends on accurately predicting the turning points between bull and bear market states.

The results of our study have significant implications for market regulators. Stock market overreactions resulting in market corrections can severely disrupt the market, leading to significant financial losses for investors and undermining investor confidence in the overall market. Further, the existence of overreactions suggests that the stock market may not always be efficient, raising regulatory concerns. Policymakers and regulators may need to implement policies and regulations to mitigate the effects of overreactions and subsequent market corrections.

This paper aims to provide additional support for the stock market overreaction hypothesis using a new setting in which this hypothesis has not been previously investigated.

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 explore the empirical relationship between the share of immigrants and the price elasticity of import demand.

We estimate the import demand function including the interaction term of the share of immigrants and relative import price, using panel data of 76 countries/areas.

The coefficient of the interaction term is significantly positive, that is, a higher share of immigrants weakens the negative effect of the relative import price on import demand. Our findings reveal the negative relationship between the share of immigrants and the price elasticity of import demand.

The share of immigrants is increasing in the present era of globalization, and it is possible that the role of exchange rate as the price adjustment mechanism in international trade become lower in the future.

This research considers different price elasticities for import goods by immigrants and natives.

In this study, we examine the influence of a firm’s environmental, social, and governance (ESG) performance on analysts’ stock recommendations and earnings forecast accuracy in the Chinese context.

We take a textual analysis approach to analyst research reports issued between 2010 and 2019, and differentiate between two distinct analyst categories: “sustainability analysts,” which refer to those more inclined to incorporate ESG information into their analyses, and “other analysts.”

Our evidence indicates that sustainability analysts tend to be significantly more likely than others to provide positive stock recommendations and demonstrate enhanced accuracy in forecasting earnings for companies with superior ESG performance. Our additional analyses reveal that this finding is particularly prominent for analysts who graduated from institutions emphasizing the protection of the environment, those recognized as star analysts, those affiliated with ESG-oriented brokerages, and forecasts made by analysts in the later part of the sample period. Our findings further indicate that sustainability analysts exhibit a more pronounced negative response when confronted with a negative ESG event.

In general, the evidence from this study reveals the interplay between ESG factors and analyst behavior, offering valuable implications for both financial analysts and sustainable investment strategies.

There are over 300,000 male migrant workers in Singapore. Around 600 major workplace injuries are reported in Singapore each year, mainly in the manufacturing and construction injuries. Migrant workers who are affected by workplace injuries often face many challenges, including not being able to work and thus may be repatriated to their home countries, which affects their financial status and that of their families, whom they support. This research aims to explore the knowledge, attitudes and beliefs of injured migrant workers in Singapore, towards disability and vocational rehabilitation.

Fifteen male migrant workers, from Bangladesh, China and India, who had acquired disabling injuries in their workplaces in Singapore, were identified through purposive sampling. They were interviewed by a male interviewer, either in Mandarin Chinese or with the assistance of interpreters for Bengali-English and Tamil-English. Interviews were recorded, transcribed, translated to English, then analysed thematically.

The interviewees generally had a pessimistic outlook on their disability, which often impacted negatively on their self-worth and familial relationships. Many of them also had little knowledge of vocational rehabilitation and had not yet seriously considered future job prospects.

To the best of the authors’ knowledge, there are no similar studies exploring the knowledge, attitudes and beliefs of injured migrant workers in Singapore towards disability and vocational rehabilitation.