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The current research takes a closer look at the investment intention of Generation Z and its relation to investing in a speculative market. The study applies the theory of planned behaviour (TPB) to understand the dominant factors leading to Generation Z investment decisions in speculative markets. The main objective is to identify whether these decisions are learnt decisions or herd behaviours.

Structural equation modelling is used to evaluate the research model, and examine the mediation effect of financial literacy using bootstrapping in AMOS software. Information was gathered from 271 students studying at the University of Technology and Applied Sciences. The questionnaire used for the survey was adapted from previous related studies examining the TPB.

The findings show financial literacy and behavioural outcome (attitude) are key components associated with investment intention. Motivation to comply (subjective norm) affects the intention to invest if mediated by financial literacy. The subjective norm has no bearing on the intention to invest in a speculative market. This implies social peers have no bearing on their intention to invest unless mediated by financial literacy.

The main limitation of the study is that the group from which the sample is drawn consists of all students at a state-funded university who receive stipends. This limits the applicability of related findings. Furthermore, the variables have dynamic properties, which implies their impacts may vary over time.

Generation Z comprises a large number of small investors who can make a significant difference to the overall economic trends of the country. The digital world, which is time- and space-infinite, is shaping the next generation. It is only possible to reach and sway their opinions by conducting extensive behavioural science research.

Academic institutions ought to be viewed as a resource for conducting additional in-depth research on a variety of subjects to assist and shape the current generation for a better future.

Although the TPB has been used by many researchers to explore the behavioural intention of Generation Z, very few have used financial literacy as a perceived behaviour control to study its direct and indirect effects on behaviour intention.

This paper aims to examine the influence of procurement physiognomies on the creative accounting (CA) of listed health-care firms in Nigeria from 2016 to 2020.

This paper used positivist paradigm. Annual reports and accounts, questionnaire and e-mails were used to obtained and extract quantitative data. The data were analyzed using OLS regression.

The study found that, procurement planning, e-procurement and procurement legislation compliance possessed negative weighty consequence on CA of quoted Nigerian health-care corporations while outsourcing, procurement staff competency and strategic supplier partnership possess positive substantial impact on the firms’ CA. The article concluded that procurement physiognomies play an important role in managing CA of health-care firms.

This study findings are only applicable to listed health-care firms in Nigeria. It only used six procurement attributes. The research implication is that researchers are to use the findings in conducting further studies on procurement physiognomies and CA to help in coming up with ways of curbing irregularities in the organizations.

The health-care firms are to use the findings to come up with policies that ensure malpractices in procurement are curbed and CA is minimized to its barest level. Its societal implication is that the public is to use the findings in changing societal attitudes toward earnings manipulation.

Its societal implication is that the public is to use the findings in changing societal attitudes toward earnings manipulation.

To the best of the authors’ knowledge, this article is the first to evaluate the influence of procurement physiognomies on CA in Nigerian-listed health-care companies. Many researchers neglect how procurement is used to carry out a lot of CA and this study focuses on a mechanism for curtailing corruption.

The study aims to examine the difference of financial-based brand equity of Sharia-compliant and non-Sharia-compliant companies listed in the stock market.

The five-year data were collected from 561 companies listed in the Indonesian stock market (349 Sharia-compliant firms and 212 non-Sharia-compliant firms).

Based on five years of observations, the study shows that Sharia-compliant companies have much higher brand equity than companies that are not Sharia-compliant. However, the study did not find consistent results when the study examined the differences between brand equity in newly listed Sharia-compliant firms in the short run (two-quarters of the observations). In other words, Sharia-compliant status positively impacted a company’s brand equity only in the long run.

The study examines only the brand equity of Sharia- and non-Sharia-compliant companies in the Indonesian stock market.

The study suggests that companies should list their equity in the Islamic stock market as the empirical evidence shows that the companies listed in the Sharia index have much higher brand equity than companies listed in the non-Sharia index, although this impact can only be seen in the long run.

The study integrates finance and marketing perspectives, which are often disconnected in daily business. In addition, the study provides a piece of empirical evidence on the effect of financial decision to be listed in the Islamic stock market on the establishment of brand equity, which represents the long-term intangible assets of the firm in the eyes of the customers.

This paper aims to examine the time-varying preferences for environment, social and corporate governance (ESG) investing in an emerging market. The investors seek ESG-conscious investments during a positive economic outlook, reflecting the time-varying nature of ESG demand. Specifically, the author shows that high-ESG stocks have negative abnormal returns during bad economic times but turn into positive abnormal returns in good economic times. The author also suggests that the alpha spread between high-ESG and low-ESG stocks is larger in good economic times than in bad times. Furthermore, individual investors prefer high ESG scoring stocks in good economic times. The author highlights that this ESG premium is shaped by economic projection and the households' financial wealth.

The economic and administrative conditions of countries normatively have an effect on the economy and level of market development. Moreover, it is of great importance for a healthy economy whether the public institutions and organizations are transparent and functioning in accordance with their purpose. The aim of this study is to show whether there is a relationship between transparency and market efficiency.

Correlation analysis has been conducted between prediction accuracy rates, which are obtained by seven different machine learning algorithms and Corruption Perception Index (CPI) levels.

It has been statistically shown that the indices of countries with low corruption levels are harder to predict, which, in turn, can be interpreted as having higher weak-form market efficiency. According to that, an intermediate negative correlation has been found between CPI scores and predictability levels of stock indices. Considering the findings, it can be interpreted that the markets of countries with relatively more transparent and well-functioning public sector have more weak-form market efficiency.

The study can be extended with cutting-edge machine learning and deep learning techniques in future studies. There are very few studies which try to explain factors related to market efficiency. Thus, the authors claim that there is still room for further research in order to determine the factors related to market efficiency, implying that current literature is still far from explaining the causation behind the inefficiencies.

According to findings, the markets of countries with relatively more transparent and well-functioning public sector have more weak-form market efficiency. Based on these findings, in practice, it can be said that more successful predictions can be made using machine learning algorithms in countries with relatively lower CPI scores.

In literature, the factors related to market efficiency are still far from explaining the causation behind the inefficiencies. Thus, it has been investigated whether transparent and well-functioning public institutions and organizations have any relation with market efficiency.

International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth anniversary, and the objective of this paper is to conduct a retrospective analysis to commensurate IJoEM's milestone.

Data used in this study were extracted using the Scopus database. Bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of a journal. Mapping of bibliographic data is carried using VOSviewer.

Study findings indicate that IJoEM has been growing for publications and citations since its inception. Four significant research directions emerged, i.e. consumer behaviour, financial markets, financial institutions and corporate governance and strategic dimensions based on cluster analysis of IJoEM's publications. The identified future research directions are focused on emergent investments opportunities, trends in behavioural finance, emerging role technology-financial companies, changing trends in corporate governance and the rising importance of strategic management in emerging markets.

To the best of the authors' knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJoEM. The study presents the key themes and trends emerging from a leading journal considered a high-quality research journal for research on emerging markets by academicians, scholars and practitioners.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

The performance of financial markets is significantly influenced by the political environment during general elections. This study investigates the effect of general elections on stock market performance in selected African markets.

Prior studies have been inconsistent in determining whether electioneering events negatively or positively influence stock market performance. The study utilized panel data set with annual observations from 1990 to 2020. The generalized method of moments (GMM) is employed to investigate the effect of electioneering and change in government on key stock market performance indicators, including stock market capitalization, stock market turnover ratio and the value of stock traded.

The study finds that electioneering activities generally have a positive impact on the performance of the stock market, whereas a change in government has a negative impact. As a result, the study recommends that stakeholders of the stock market remain vigilant and actively monitor electioneering events to devise and implement effective policies aimed at mitigating political risks during general elections. By adopting these measures, investor confidence can be significantly enhanced, fostering a more robust and secure investment environment.

The study investigates a neglected section of the literature by highlighting not only the effect of elections on stock market indicators but also possible change in government during elections.