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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.

Long memory suggests that past returns can influence future returns over extended periods, indicating potential predictability. The aim of this study was to empirically examines the interplay between long memory effects, downside risk, and stock returns across five major international equity markets (S&P 500, FTSE 100, Nikkei 225, DAX, and ASX) from 2000 to 2022. An ARFIMA model which quantify fractional differencing was used in conjunction with a correlation matrix, regression and Granger causality test to investigate the interplay. The findings revealed a significant long memory across all markets, with Nikkei 225 exhibiting the strongest persistence, attributed to Japan’s prolonged economic stagnation. Counterintuitively, lagged downside risk positively predicts returns, aligning with behavioural theories of investor overreaction to tail events. Bidirectional causality between risk and returns underscores reflexive market dynamics. The study bridges a critical gap by integrating long memory into downside risk models, demonstrating that persistent volatility amplifies tail risk premiums. The findings of this study suggest that financial market regulators need to seriously considers adaptive regulatory tools due to the dual long memory in returns and downside risk necessitates. To the author´s knowledge, this study is the first to bridge a critical gap in the literature by systematically integrating long memory effects with downside risk metrics so as to provide a more robust framework for understanding risk-return dynamics, as it accounts for the persistent nature of volatility and tail risk, which traditional short-memory models often overlook.

The national development plan (NDP) clearly states that communities should take initiatives to solve their own problems. One of the socio-economic problems faced by communities in South Africa is the effects of inequality, which is largely the apartheid era is responsible for. In trying to deal with the socio-economic problem of inequality, many communities in South Africa have resorted to forming and running cooperatives in different sectors of the economy. However, these cooperatives are reported to be facing numerous challenges, and in some cases, many of them fail or close down. The challenges mentioned range from a lack of capacity to manage, poor economic performance in the country, heavy reliance on government grants and many others. Despite the challenges faced by cooperatives in South Africa, they are still believed to be a very important tool in dealing with the problem of inequality, which mostly affects rural areas where most of these cooperatives operate. To understand the potential impact of cooperatives in dealing with inequality, this paper uses a qualitative approach. Secondary sources of published information by government agencies and cooperatives themselves will be used, as well as literature published by experts in the area of study. Preliminary findings of the paper are that cooperatives in South Africa are very small in size, and the majority of them are in primary sectors of the economy. If cooperatives are to play a significant role in dealing with inequality, there is a need for them to be capacitated to grow and also participate in upper value chains of the economy.  

Financial markets are a system of complex price dynamics that are often influenced by various nonlinear factors. Traditional linear models such as Autoregressive Moving Average also known as ARMA and the GARCH model often fail to capture the inherent nonlinearities observed in financial market returns and trading volumes which poses significant problems in modelling financial time series. The aim of this study was to investigates the role of threshold nonlinearity in modelling financial market returns and trading volume. Daily share prices and trading volumes from the S&P 500 and the Nasdaq indices spanning from January 2000 to December 2020 were used as the sample financial markets. The findings revealed that the threshold model improves predictive performance over traditional linear models, highlighting the importance of incorporating nonlinearity for understanding the dynamics of financial markets. A threshold autoregressive model was proposed to account for sudden shifts in market conditions and nonlinear dependencies between returns and trading volumes.

Prior research on volatility spillover indicates that financial market spillovers are not uniform across all markets. Their intensity and direction depend on factors such as market size, economic linkages, and the nature of the shock. The aim of this study was to comprehensive investigation of volatility spillovers across five major international equity markets from January 2008 to January 2024, a period encompassing multiple financial crises. Utilizing a conditional volatility model, followed by the construction of a spillover index and Granger causality tests to investigate the direction and magnitude of volatility transmission, the findings of this study revealed that the S&P 500 is a net receiver of volatility, acting as a global shock absorber rather than a primary transmitter. In contrast, European markets and the Nikkei 225 emerged as net transmitters, with a tightly coupled, bidirectional relationship observed between the United Kingdom and European markets. The Shanghai Composite was also found to be nearly neutral, likely due to capital controls. These results reiterated the notion that volatility transmission networks are dynamic and not solely dictated by market size. The findings of this study suggest that asset managers and other investors can significantly increase the value of their portfolios if they adopt a multi-polar risk management framework, moving beyond a United States centric view for international diversification to include more European and Asian markets assets in their portfolios.

Traditional financial models assume constant variance in asset returns however, empirical evidence highlights volatility persistence, which are effectively captured by ARCH and GARCH models. The aim of this study was to examine the heteroskedastic behaviour of stock prices across international financial markets. Using a GARCH (1,1) model with a student’s t-distribution, this study analyses daily stock returns from the NYSE, FTSE 100, Nikkei 225, and Shanghai Composite (SSE) between 2018 and 2023. The findings confirm significant autoregressive conditional heteroskedasticity in all markets, supporting the notion that financial volatility is dynamic and clustered. Developed markets, such as the NYSE and FTSE 100, exhibit lower baseline volatility but more persistent shocks, indicative of market efficiency and stability. In contrast, emerging markets, particularly SSE, demonstrate higher volatility and greater sensitivity to past shocks, reflecting heightened uncertainty. The results re-iterate the importance of incorporating dynamic volatility models in risk assessments. Given the increasing complexity of global financial systems, this study advocates for enhanced regulatory frameworks and adaptive econometric methodologies to account for evolving market structures and external macroeconomic shocks.

High-frequency data offers unparalleled insights into market dynamics, facilitating the analysis of statistical properties, dynamic correlations, and scaling behaviours with a precision previously unattainable. These Statistical Properties are essential framework for understanding and modelling the interactions between global financial markets over time. The aim of this study was to investigate the dynamic conditional correlations and scaling behaviour of high-frequency data from January,1 2010 to December 31, 2020. Using the S&P 500, FTSE 100, Nikkei 225, HKEX and DAX as sampled financial markets, the results revealed significant differences in correlation patterns across the markets, as well as fractal-like scaling behaviour. The relationship between the S&P 500-FTSE 100 and S&P 500-DAX supports trend-following strategies, while the Nikkei 225-HKEX and Nikkei 225-FTSE 100 may be closer to random-walk behaviour. This study advances the frontier of knowledge on high-frequency data and offers insights into the temporal relationships and scaling properties between financial markets.

Capital structure defined by the proportion of debt and equity financing has always been an important component in managerial finance due to its relative importance in measuring a company's risk. Arguably, it is among the most controversial topics in corporate finance and has gain recent recognition of its importance when considering the emergence of different sources of financing across industry. The purpose of this study was to explore the relationship between capital structure and a firm's profitability in other to determine whether a significant connection exists between how a firm finances its operations and its financial performance. A comprehensive Lilliefors’s, Kolmogorov Smirnov and Chi square tests analysis were utilised for a sample of 23 peer reviewed journal articles across various industries. The findings revealed that capital structure cannot be used to explain variations in profitability and profitability is independent of capital structure. This paper contributes to the ongoing debate on optimal capital structure by strengthening methodological rigor and some of the existing theories on how capital structure can be used to maximise profitability. It may be very helpful for further studies to first establish the direction of impact and the size effect of the independent variables.

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.

Financial markets areconstantly fluctuating in response to a range of factors. These fluctuations connote volatility which are unpredictable behaviour that deviates significantly from the patterns observed in stable conditions. It is therefore important to use standard stochastic volatility models to capture the time-varying nature of volatility during periods of financial distress in other to make informed decisions. The aim of this study was to explore stochastic volatility in financial markets during periods of financial distress using a Heston model. The sample financial markets were, the CAC 40, the DAX, the JSE, the Nasdaq Index and the Nikkei-225. The 2007/2008 financial crisis and the Covid-19 pandemic were used as sample time frames. The findings of this study revealed extortionate stochastic volatility during the Covid-19 pandemic compared to the 2007/2008 financial crisis. Since the 2007/ 2008 crisis stemmed from a collapse in the financial system, the nature of the event was more localized within the financial sector and had a somewhat more predictable unfolding than the Covid-19 pandemic. By Implication, it may be suggested that it was more difficult to price assets during the Covid-19 pandemic due to extreme uncertainty which resulted in inflated option premiums as investors sought hedging strategies.

The market resilience of a stock is an integral component of financial market because it supports smooth trading and absorb market shocks. However, the contention has always been to effectively determine the level of resilience in stocks with the application of proper methodology to make informed decisions. Therefore, aim of this study was to examine the level of market resilience in selected stocks in the Johannesburg stock exchange. Using a fixed effect model, the findings of this study indicates that most securities trading on the exchange lack market resilience. This was evident in the significant relationship between the specific independent and dependent variables. A risk coefficient should therefore be assigned to bid and ask to spread to enhance market resilience. These risk coefficients will induce the bid and ask prices to be close to the fundamental price.

The market tightness of equity securities is an integral component of financial market because it supports smooth trading and absorb market shocks. However, the contention has always been to effectively determine the level of tightness in equity securities with the application of proper methodology in order to make informed decisions. Therefore, aim of this study was to examine the level of market tightness in selected stocks in the Johannesburg stock exchange. Using a variance ratio model, the findings of this study indicates that most securities trading on the exchange lack market tightness. This was evident in the difference between the one, two and three months variances that were not equal to 1 . Therefore, a specialist system should be introduced in order to facilitate the execution of trades without significantly moving the prices of equity securities. This study improves the frontier of knowledge regarding liquidity management, by empirically investigating the market tightness of equity securities.

Internationally, migrants encounter a multitude of obstacles in the countries they settle in, including being denied access to essential services and economic prospects due to restrictive laws that undermine their ability to contribute to the fulfilment of the 2030 global agenda. While it is crucial to incorporate migration into development goals at all levels, the integration at the local government level is still incomplete. This research utilises the notion of global governance and employs a qualitative case study approach to conduct an explorative investigation of the difficulties pertaining to the integration of migration into municipal development plans. A total of 20 individuals engaged in municipal development planning were interviewed. The data was collected by reviewing municipal strategic plans. Thematic analysis was employed for data analysis. The study findings indicate that the lack of current migration data, negative attitudes towards migration, and insufficient coordination among parties responsible for development planning are the primary obstacles to integrating migration into municipal development planning. The exclusion of migrants has significant ramifications for municipal planning. Not only does it contradict the 2030 global goal, which recognises migrants as catalysts for sustainable development, but it also renders municipal planning inadequate and unsustainable. The discoveries aided in the creation of the Migration and Municipal Development Planning Framework. The framework has substantial ramifications for municipal planning since it delineates the actions that local municipalities could take to seamlessly incorporate migration issues into development planning, along with the anticipated results. The framework is a unique and valuable addition to the existing knowledge since it offers a fresh viewpoint on the relationship between migration and sustainable development, specifically at the level of local government.

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

The efficient market hypothesis assumes that financial markets fully incorporate all available information, rendering past information irrelevant for predicting future prices. However, numerous studies challenge this notion and suggest the presence of long-term memory in market dynamics. Understanding long-term memory in financial markets has important implications for investors and policymakers. The aim of this study was to empirically investigate long term memory in financial markets. This study employed a Hurst model for a sample of 5 financial markets from June 1, 2018, to June 1, 2023. The findings revealed that four out of the five sampled financial market exhibits long term memory which challenges the efficient market hypothesis concept. Therefore, portfolio managers and active market participants can utilize long-term memory to optimize asset allocation decisions by considering the persistent effects of past returns and adjust portfolio weights to take advantage of potential return predictability and manage risk.

There has been a proliferation of numerous investment strategies over the years, but one strategy is perceived to have stood the test of time. Investing in the foreseeable future is favored by many successful long-term investors which focus on maintaining a long-term investment horizon. Investing in the long term involves purchasing assets and holding onto them for an extended period, regardless of short-term market fluctuations. Exploring the benefits of this method can help investors make informed selections and build a disciplined investment mindset to achieve their financial goals. The goal of this study was to empirically evaluate the merits of a purchase and hold strategy in financial markets because of its perceived considerable influence on active market players. A Sharpe ratio was utilized for six financial markets from June 13, 2018, to June 13, 2023. The findings revealed that market participants can achieve a sustainable return by simply maintaining a long-term view of their portfolios. This was evident in the Nasdaq, CAC 40 and Nikkei 225. Upon selecting a particular risk tolerance and investment horizon, market participants may earn significant returns on their portfolios.

To date, there are many myths surrounding security selection and fundamental analysis due to the notion that all available information is perfectly priced in security. However, fundamental analysis is still very useful in determining the financial health of security, which is much needed for long-term investing. The aim of this study was to perform a valuation analysis of international stock markets so as to recommend buy or sell signals suitable for investment decisions. This study made use of the coefficients of a multiple regression analysis and value drivers for the S&P 500, Nasdaq, JSE, DAX, Nikkei 225, BIST 100, FTSE, and SSE as of January 23, 2023. The findings revealed that the JSE, DAX, Nikkei 225, and FTSE are undervalued, which translates to a buy signal, while the S&P 500, Nasdaq, BIST 100, and SSE are overvalued. These findings imply that investing in the JSE, DAX, Nikkei 225, and FTSE will produce decent capital gains on a risk-adjusted basis.