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A recurring theme in the literature and discussion of stock market analysis is the concept of the January effect which essentially implies buy in December and sell in January. The January effect is based on the perceived seasonality trend where short term investors hope to take advantage of the arbitrage opportunity between December and January. However, this perceived anomaly continues is seen as a myth and has been strongly rejected by many market participants in the current dispensation. The purpose of this paper was to investigate the January effect in the Nikkei 225, JSE, CAC 40, DAX, and the NASDAQ Index from February 2019 to February 2024. A F-test statistics was used to explore the phenomenon. The findings indicated that there is no significant difference between the realised returns in January and those of the other month for the most recent five years. This led to the conclusion that, while the January effect may have been observed, it is no longer evident in financial markets, hence market participants should avoid using this type of arbitrage approach because it may result in massive losses

Modelling volatility using asset price returns has always been at the forefront of financial economics and option pricing. Observing the conditional variance properties in these asset returns, can be very useful for trend analysis and volatility predictions which are ever needed for trading, portfolio management and financial decision making. The aim of the study was to model and forecast volatility in stock markets. Six financial markets namely the Nasdaq, JSE, the DAX, the CAC 40 and the Nikkei 225 were used as samples with a sampling frame from January 29, 2018 to January 29, 2023. The findings of this study revealed that the variance for all the financial markets under consideration changes significantly with the passage of time. Also, volatility in the JSE, DAX & CAC 40 display fat tail distributions and it is expected to move by three standard deviations. Accordingly, volatility will persist in the Nasdaq, DAX and CAC 40 at an increasing rate but will persist at a decreasing rate in the JSE and Nikkei 225. Considering the peril involved in stock market investing, this study makes a notable contribution to estimating market volatility which is an integral component of asset pricing. With this knowledge, analyst and market traders will have a better understanding of the error distribution in stock markets which will assist in specifying predictive asset prices.

Conventional intrusion detection systems encounter difficulties in addressing advanced cyber threats and handling the increasing volume of network data.  This research presents a modernisation strategy by integrating swarm intelligence algorithms to enhance the efficiency and efficacy of intrusion detection.  This research employs qualitative observational and content analysis methodologies to investigate the utilisation of swarm intelligence in improving intrusion detection systems.  Findings demonstrate substantial enhancements in detection rates and system efficacy, with swarm intelligence algorithms attaining a true positive detection rate of over 99% and minimising false positives to as low as 2%.  These findings highlight the impending substitution of conventional intrusion detection systems with swarm intelligence-based alternatives, offering significant enhancement in cybersecurity capabilities.

The Fourth Industrial Revolution (4IR) has profoundly impacted education systems globally, including in Africa. However, there is a dearth of research on Education 4.0 in Africa. To fill this gap, a bibliometric analysis of Scopus database data was conducted, with VOSviewer used to analyze the data. The study found that African research outputs on Education 4.0 have increased linearly between 2015 and 2022, but most are published outside Africa. Limited collaboration among African institutions and academics was observed, with African authors citing more academics outside Africa than within. E-learning, machine learning, cloud computing, and artificial intelligence were the most frequently researched areas. This study provides crucial information to researchers and policymakers, allowing them to map out future research and policies on Education 4.0.

The dynamics of investing in stock markets have changed considerably over the past twenty years. The value and growth investing concept has drawn much attention and continues to dominate portfolio management. Factor investing is an approach that aims at quantifying particular characteristics through matrixes of quality, value, momentum, and growth to improve the returns of a portfolio. Although the three-factor style of investing has gained widespread acceptance, the five-factor continues to be a source of disagreement among academics and industry practitioners. This study aimed to review the merits of five-factor investing critically. A critical literature review was used to review prior literature. The narratives from the literature reveal that the five-factor investing style will result in multiple sources of expected return. Hence, using five factors in portfolio formation has merits and will significantly improve the returns and most likely outperform the market portfolio. Factor premiums have proven to be drivers of asset return. Therefore, market participants and investment managers can intentionally assume additional exposures to deliver superior risk-adjusted returns compared to the market with the five-factor investing style. According to the author's knowledge, this is the first study to objectively analyze the literature on five-factor investing and propose that active managers can earn positive alphas on a risk-adjusted basis through factor exposures.

The rapid integration of Artificial Intelligence (AI) into higher education is fundamentally reshaping how students access and present knowledge. Within legal education particularly, AI tools increasingly enable students to generate summaries, conduct research, draft legal content and retrieve information with minimal engagement in traditional legal reasoning processes. At the same time, South African law faculties remain tasked with cultivating industry ready candidate legal practitioners who possess critical thinking abilities, ethical judgment, persuasive communication skills as well as strong legal research competencies. This creates an important pedagogical tension. While AI may enhance efficiency and technological fluency, its uncritical use may simultaneously undermine the intellectual rigour required for competent legal practice. This paper critically examines whether the growing integration of AI in legal education supports or compromises the cultivation of graduate attributes required within the South African legal profession. Using a doctrinal and conceptual methodology, the study analyses the graduate competency frameworks advanced by the Council on Higher Education and the South African Qualifications Authority. It further interrogates whether AI tools meaningfully contribute toward the development of legal reasoning, ethical sensitivity, drafting proficiency and contextual interpretation, all of which remain central to professional legal readiness. The paper argues that although AI presents significant opportunities for enhancing legal research efficiency and technological competence, overreliance on these tools risks producing graduates who are technologically capable yet jurisprudentially underdeveloped. It contends that AI cannot replace the human processes of critical engagement, ethical reflection and contextual legal interpretation that define legal education within a constitutional democracy. The study concludes that responsible AI integration in legal education requires careful pedagogical design and ethical oversight to ensure that innovation does not come at the expense of professional competence and intellectual integrity.

The study sought to relationship between enterprise risk management practices (ERM) and perceived sustainability of emerging and junior mining sector in South Africa as it creates employments and mineral diversification. This sector remains vulnerable to various risks factors including financial and regulatory among others.  The objective of the study was to examine the relationship between enterprise risk management practices and perceived sustainability of emerging and junior mining sector in South Africa. The study was anchored upon the governance theory. The study adopted the use of a quantitative methodology with a cross-sectional design using a secondary data panel downloaded from Kaggle data repository. The sample size of the study was 500 which included 262 males and 238 females who work in different mining organisational sizes. Regression analysis and structural equation modeling were tested and run to test the hypothesised statements. The results of OLS regression model 1 show that ERM_Governance has a positive and significant effect on sustainability and ERM_Value shows a negative and significant association, and other ERM dimensions are positive but not statistically significant. OLS regression Model 2 for perceived socio-economic importance shows that ERM_learning is a positive and significant predictor of socio-economic importance, and other ERM dimensions are not significant in this model. On the SEM front, ERM practices exhibited strong positive effects on both perceived sector sustainability and socio-economic importance (p < 0.001). Perceived sector sustainability significantly mediated the relationship between ERM practices and socio-economic importance (p < 0.001). Additionally, organisations size and mining experience were found to significantly moderate the ERM-sustainability relationship (p < 0.05), indicating the presence of contextual effects within the emerging and junior mining sector. The theoretical implication suggests that ERM should be taken as a monolithic construct but rather as a set of complementary dimensions with distinct outcome pathways. Practically, the results imply that policymakers and mining executives should prioritise strong governance and accountability systems to enhance sustainability perceptions while simultaneously investing in learning and collaborative capabilities to maximise socio-economic impact.

Cross-sector fraud between South Africa’s financial and telecommunications sectors has intensified, driven by sophisticated criminal networks that exploit operational disparities between these industries. Traditional fraud detection systems, which depend on centralised data sharing and siloed organisational structures, encounter substantial legal and practical obstacles under the Protection of Personal Information Act (POPIA). These include restrictions on cross-entity data transfer, data minimisation requirements, and transparency obligations for automated decision-making. This study developed and evaluated a privacy-preserving framework that integrates Federated Learning (FL) and Explainable Artificial Intelligence (XAI) to enable collaborative fraud detection while safeguarding customer privacy and ensuring regulatory compliance. A mixed-method approach was employed. Quantitative experiments were conducted on a dataset of 35,675 credit card transactions linked to telecommunications parameters, training a federated global model across simulated financial and telecommunications providers. A qualitative systematic literature review identified key barriers to cross-sector collaboration and distilled lessons from prior Federated Learning and Explainable Artificial Intelligence implementations. The federated model demonstrated strong detection performance, achieving a ROC-AUC of 0.98, near-perfect precision and recall for legitimate transactions, and an F1-score of 0.89 for the minority fraud class, all without exchanging raw customer data between institutions. SHAP analysis identified TransactionAmt and card1 as the most influential features, thereby satisfying POPIA's transparency mandates. While federated approaches did not consistently surpass siloed models in raw accuracy, this marginal trade-off reflects the essential privacy-utility equilibrium required for legal compliance. The framework effectively reconciled collective security with individual privacy, confirming that POPIA adherence need not diminish detection efficacy. This study provides a validated blueprint for financial and telecommunications providers in South Africa to collaboratively counter cross-sector fraud within regulatory constraints. The framework further supports audit trail generation and decision rationales, addressing compliance obligations. Drawing on systematic literature review insights, particularly regarding collaboration barriers and prior Federated Learning and Explainable Artificial Intelligence implementations, the findings offer regulators and investors practical guidance for meeting statutory requirements without compromising privacy rights.

The study examined the critical approach adopted by entrepreneurship and policy development in promoting socio-economic development. In this review. It also looked at the difficulties small, micro, and medium-sized businesses in South Africa face. The conceptual research looked at the influence of small, micro, and medium-sized companies (SMMEs) on South African development. The study's objective was to evaluate how small, medium-sized businesses (SMMEs) are now driving economic growth and solving social issues in a setting where big businesses and governments appear to be doing very little. As a result, the study examines how SMMEs might help the nation solve persistent societal evils, including poverty, inequality, and social isolation. A desktop literature review research design was adopted to collect data on the role of entrepreneurship in South Africa. The study further looks at policies and practices of big business and government that exacerbate social issues in communities and impede socio-economic development, as well as the specific roles that SMMEs play in achieving socio-economic development goals. The findings reveal that, among various developmental outcomes, SMMEs are at the forefront of generating employment and promoting social and political justice within the nation. Future empirical studies should account for and evaluate the importance of SMME contributions to constructing economic, social, and political justice in the nation.

Market participants usually seek exposure to global beta which are best achieved usually through large capitalization equities where higher idiosyncratic risk represents a potential source of alpha. This study empirically explored the persistence and drivers of idiosyncratic risk across the S&P 500, CAC 40, FTSE 100, DAX, and Nikkei 225 for a period of 20 years (2004 – 2024). Utilizing a quantitative risk decomposition framework, the analysis revealed a stark dichotomy where the S&P 500 exhibits minimal idiosyncratic risk, functioning as a proxy for global systematic factors. The Nikkei 225 was dominated by idiosyncratic risk which can be attributed to the unique domestic monetary policy. The European counterparts occupied a middle ground, with idiosyncratic risk amplified by sectoral concentrations. The correlation analysis further suggested a deep regional integration within the European markets and a diversification potential with the Japanese equity market. The findings challenged the notion that globalization has harmonized global financial market returns. These findings also demonstrates that idiosyncratic risk remains a significant and potentially compensable component of the concept of international investing, necessitating a strategic shift towards factor aware asset allocation.