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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.