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The purpose of this paper is to investigate the relation between the investment horizon of institutional investors and corporate social responsibility (CSR).

Utilizing unique datasets on CSR and the investor horizon measures (Gaspar et al., 2005), the authors categorize institutional investors into long-term and short-term investors. This method captures the heterogeneity of investors.

The authors show that long-term institutional investors promote CSR engagement, while short-term investors discourage it. The authors further document that shareholders’ ownership horizon has implications on corporate decisions in the CSR framework. The presence of long (short)-term institutional investors is positively (negatively) associated with dividend payout, discourages (encourages) managerial misbehaviors and enhances (reduces) firm valuation, only for firms with high CSR performance.

Different from previous studies that treat institutional investors homogeneously, this paper provides empirical support that investors are indeed different in influencing CSR.

Few prior studies address the question of whether active engagement by institutional shareholders on CSR issues differs by the types of institutional ownership. The study attempts to fill this gap by examining the effects of institutions’ investment horizon, one of the major ways to classify institutional shareholders, on the CSR performance of firms.

This paper aims to examine the monitoring role of institutional investors in corporate decision-making by classifying financial institutions based on geographical proximity and investment horizon from 1980 to 2014.

By using unique data sets on firm and institution location and investor horizon measure (Gaspar et al., 2005), the authors categorize institutional investors into six proximity-horizon classifications. This method captures the heterogeneity of investors. The corporate decisions assessed include firm investment, financing, payout policy, misbehavior, takeover defenses and profitability.

Both geographical proximity and investment horizon are directly related to institutional investors' monitoring cost. As a result, the effectiveness of institutional monitoring may vary based on geographical proximity and investment horizon. This paper collectively examines both dimensions of financial institutions and provides evidence that institutional investors present different preferences for corporate policies. Given stronger information advantage, both local and nonlocal investors that are long-term oriented fulfill better roles in monitoring corporate decisions but from different perspectives.

Different from previous studies that treat institutional investors homogeneously, this paper provides empirical support that investors are indeed different in influencing firm policies.

To the authors’ best knowledge, this is the first study that classifies investors based on two dimensions, geographical proximity and investment horizon, and examines their joint effects on corporate policies. This proximity-horizon classification allows the authors to better disentangle the effects of institutional ownership structure on the monitoring outcomes.

This study aims to examine the relationship between corporate social responsibility (CSR) and measures of financial reporting quality.

The authors explore the link between CSR and several indicators of firms’ financial reporting quality. Estimation with firm and year fixed effects is based on a sample of US publicly traded firms covering the period from 1991 to 2018.

Empirical results demonstrate that firms with higher CSR scores are associated with higher accuracy of financial forecasts, fewer earnings surprises and greater coverage by financial analysts. This positive relationship is more profound for firms that face low agency concerns, firms that have a higher level of customer awareness, firms that have more long-term institutional ownership or firms that do not face financial constraints.

The study contributes to the ongoing debate on the value of CSR. The results support the stakeholder value maximization view of CSR and identify the impact of several factors on its relationship with the quality of financial reporting.

National student loans help solve the problem of tuition fees for students from poor families to a great extent. This paper aims to study the behavior of three main players involved in university student loans, namely, universities, banks and students and explores necessary conditions for promoting the steady development of student loans, as well as the sustainability of cooperation and coordination among players, thus promoting the further development of student loans.

First, from the perspectives of the three related players of banks, students and universities and their behavior, this paper establishes a three-player behavioral evolutionary game model, conducts a sustainable game analysis among the different players, and by replicating the dynamic equations with the Jacobian matrix solve the evolutionarily stable strategy. Finally, applying MATLAB tools, a sensitivity analysis of relevant impacting factors is carried out to explore the influencing mechanism of the sustainable development of student loans.

To achieve the mechanism of mutual coordination and cooperation between participants, banks need to be guided to actively issue student loans and conduct strict loan review. College students should be encouraged to establish good credit and strengthen penalties should be implemented for violations of regulations. Universities should be encouraged to help banks reduce information asymmetry, promote financial knowledge and student integrity education and promote the sustainable development of national student loans.

This research will help scholars better understand the interaction mechanism among universities, banks and students, and promote the sustainable development of national student loans.

The crypto market is growing quickly, marked by a lack of fundamentals, and the risks are not yet fully comprehended by participants. Our goal is to investigate overconfidence in this market and analyze the role that risk propensity and certain demographics play.

We conducted a survey in Brazil and Portugal, leveraging an online questionnaire disseminated via social media channels to engage a diverse adult population. We collected a total of 826 responses, addressing ethical considerations throughout the process. The data analysis was conducted using SPSS statistical software and logit regression modeling.

Our study reveals that overconfidence is a notable bias that distinguishes individuals who invest in cryptocurrencies from those who do not. Although overconfidence and risk propensity are closely linked, they originate from distinct personal characteristics. Furthermore, our findings indicate that age and market experience positively correlate with overconfidence and negatively correlate with risk propensity. Financial knowledge, interestingly, did not prove to be a significant factor for cryptocurrency investment.

Our research augments the existing literature on overconfidence, delving into this phenomenon in a new subdomain, and in doing so, it enriches our comprehension of the unique and still relatively under-researched cryptomarket. Moreover, we illuminate individual factors that sway the decision to invest in cryptocurrencies and should be considered by market participants.

• (1)

  Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.

• (2)

  Verifies the relations among overconfidence, risk propensity, and demographics.

• (3)

  Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.

• (4)

  Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.

Verifies the relations among overconfidence, risk propensity, and demographics.

Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.

Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

Nowadays, Bitcoin is facing many environmental problems arising from the proof of work based on blockchain. For this reason, Bitcoin Green (BITG) has been created and would solve these issues. The purpose of this paper is to visualize the users’ perception toward BITG through Twitter text analysis.

The big data used in this study includes two sources. The first data were extracted from the “Google Trends” engine during the period between 20 September 2015 and 15 September 2020. The second data were extracted from the Twitter application. This research explores the perceived ease of use, the perceived usefulness, the social influence, the perceived control and the user attitudes toward BITG. Therefore, lexicon-based sentiment analysis techniques combined with different dictionaries are built to visualize the drivers of investor attitudes toward the BITG using Twitter text messages. Besides, this study has checked the validity of two main assumptions using the normality (Jarque-Bera) and Kruskal-Wallis rank sum tests capable to conclude whether users mostly perceive BITG as a sustainable technology.

This empirical work affords insights into users’ intentions by exploring the drivers of BITG perception. The results show that users positively perceive the use of BITG as a sustainable blockchain. Besides, its usefulness is more appreciated from its ethical and technological characteristics, and its perceived application is mainly based on investment and coin offering use. Similarly, users are mostly showing positive emotions toward BITG.

Tweets related to “BITG” are not as voluminous as the other cryptocurrencies like Bitcoin and Ethereum, which make it difficult to extract all the characteristics and use cases.

To the best of the authors’ knowledge, this work is the first one that uses the theory of planned behavior and the theory of acceptance model to explore cognitive factors in understanding investor intentions in adopting BITG.