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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.

Traditional asset pricing models assume rational investors and informational efficiency. However, persistent anomalies, and recurrent financial crises challenge these assumptions. This study synthesizes findings from leading international research to evaluate how cognitive biases shape asset prices and provides a comprehensive review and synthesis of behavioral asset pricing in international financial markets over a period from 2004 to 2024. Evidence from the extensive stock market data, cross-market behavioral heterogeneity and survey-based risk premia consistently demonstrates that behavioral effects are time-varying and regime-dependent. The findings indicate that behavioral heterogeneity intensifies during boom and bubble periods, diminishes during crashes, and generates predictable patterns in returns across asset classes and countries. Overall, the study re-iterates support for a dynamic behavioral asset pricing framework that integrates psychological factors into international financial market analysis.

The purpose of this study was to investigate the efficacy of hybrid forecasting models that integrate the classical Autoregressive integrated moving average framework, the support vector machines, the artificial neural networks, and random forest for predicting S&P 500 index returns. Utilizing daily share price data from 2015 to 2023, the methodology used a two-stage decomposition process where ARIMA captured linear dependencies, while the ML models projected nonlinear patterns within the residuals. The Out-of-sample analysis revealed that all the hybrid models significantly outperformed their standalone ARIMA benchmark across key accuracy metrics, such as mean absolute error, root mean squared error, and directional accuracy. The ARIMA-RF hybrid achieved the highest predictive accuracy and statistical significance, though it entailed a greater computational cost. The findings confirmed that the hybrid forecasting  capabilities to mitigate linear autocorrelation and ARCH effects, offering a robust approach for financial time series forecasting.

Employee attrition remains a persistent challenge for ICT organisations in Gauteng, South Africa, impacting performance, continuity, and workforce stability. High turnover disrupts project timelines, erodes institutional knowledge, and inflates recruitment and training costs. This study investigates attrition predictors by applying logistic regression to a synthetically generated structured dataset of 1,470 employee records in Gauteng. It examines whether workplace, demographic, and relational factors significantly influence turnover outcomes. Guided by three work-based practice frameworks: Social Exchange Theory, Herzberg’s Two-Factor Theory, and Job Embeddedness Theory, the research aims to identify meaningful retention drivers beyond surface-level attributes. A stratified random sampling method was used to construct a representative subset of 50 records, balancing class proportions within practical constraints. Pre-processing, encoding, and scaling were performed using Python libraries to ensure data integrity, followed by hypothesis testing and regression analysis to evaluate predictor significance. Findings show that relationship satisfaction and organisational commitment negatively correlate with attrition, reinforcing the importance of intrinsic motivators in retention. In contrast, gender, age, tenure, and income-related variables were statistically insignificant, challenging conventional HR assumptions. These results contribute to organisational behaviour literature by highlighting relational and psychological factors over demographic predictors. They suggest that fostering a supportive work environment and cultivating employee engagement may be more effective than relying solely on compensation or tenure-based incentives. The study recommends future research that incorporates larger samples from sectors beyond ICT, employs hybrid methodologies and longitudinal designs, and integrates qualitative insights from interviews or surveys to deepen understanding and inform proactive retention policies.

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 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.