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Volume XX, Spring, Issue 1(87), 2025
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In this research, we examine the immediate and long-term implications of the Sarbanes-Oxley Act (SOX) on the provisions of state takeover laws. We gather the information from the 1998–2006 IRRC corporate governance database and split it into three time periods: pre–SOX (1998–2000), post–SOX (2002–2004), and long–SOX (2006). We find that while there were some notable changes in the states' addition of opt-in and opt-out options for some of these laws, there were no significant changes in the way these laws were applied between the pre-SOX and post-SOX periods. Additionally, we discover that SOX had no significant further effect on either the laws themselves or their opt-in and opt-out clauses beyond 2004. Our findings suggest that states granted the companies extra latitude regarding some takeover defense clauses shortly after the SOX legislation went into effect.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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Stock price prediction is a critical yet challenging task in financial markets due to the complexity and volatility of asset movements. This paper presents a hybrid approach that combines Recurrent Neural Networks (RNN), particularly Long Short-Term Memory (LSTM) models, for time-series prediction with Transformer-based text analysis to capture sentiment from financial news. The study focuses on predicting Apple Inc.'s (AAPL) stock price, using three years of historical data alongside news sentiment analysis. The LSTM model captures temporal dependencies in the stock prices, while the Transformer model extracts relevant features from unstructured textual data, offering insights into market sentiment and external events. The results demonstrate that integrating sentiment data with stock price predictions significantly improves model accuracy, as reflected by a reduction in mean squared error (MSE) compared to models based solely on price data. This hybrid model offers a more holistic approach to financial forecasting, combining quantitative and qualitative data for enhanced prediction.
The paper contributes to the field of machine learning in finance by highlighting the benefits of hybrid modelling approaches, and it opens avenues for future research on broader applications in other asset classes and more diverse data sources.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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The aim of our study is to analyse the heterogeneous effects of exchange rate regimes on real exchange rate misalignments in Africa. The BEER approach is employed to determine equilibrium exchange rates and the degree of misalignments. We use the Grouped Fixed Effect estimator. The study relies on annual data covering 37 African countries over the period 1996-2019. Considering Rodrik (2008) and the Balassa-Samuelson effect, two groups of countries have been identified endogenously. Furthermore, irrespective of the classification (country group or income level), fixed exchange rate regimes have a positive impact on misalignments, while intermediate and flexible exchange rate regimes have a negative effect. Additionally, the impact of flexible exchange rate regimes is greater than that of intermediate exchange rate regimes. Thus, African countries should prioritize floating regimes (intermediate and flexible) to contain the level of misalignment. The major innovation of this study lies in the use of the Grouped Fixed Effect estimator. This allowed for extending the study to cover the entire African continent, unlike previous studies.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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The present paper provides the comprehensive review of co-location facility and related technical glitch or scam at National Stock Exchange (NSE). The previous technological advancements such as algorithmic trading (AT) and high-frequency trading (HFT) contributes the market positively by controlling volatility and as liquidity provider. Particularly, Algorithmic trading has been praised for providing liquidity and controlling volatility, particularly for retail traders. However, some argue that it harms both small and institutional traders and the market's order. This article analyses the influence of co-location on the major characteristics affecting market quality: Price discovery, liquidity, transaction costs, volatility, and punishing slower traders. The findings of the paper suggest that co-location is not a scam it is a glitch of servers which has given loopholes to the institutional traders and ultra-speed of information flow.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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Social media advertising plays a crucial role in brand development within consumer awareness, influencing purchase intentions, and is emphasised and utilised by businesses in their strategies. The objective of this study is to analyse the impact of social media advertising on consumers' purchase intentions in Ho Chi Minh City. The research employs two main methods: qualitative and quantitative. Data was collected from 656 consumers living in Ho Chi Minh City. The results show that there are six factors - entertainment value, informativeness, celebrity endorsement, interactivity, trust, and brand image of social media advertising - that affect consumers' online purchase intentions in Ho Chi Minh City through the mediating variable of loyalty. Based on the research results, several solutions are proposed to help businesses realise that social media advertising can enhance their sales and potentially achieve brand loyalty from consumers.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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This study examines volatility spillover across sectoral stock indices in India, an emerging market economy, during the COVID-19 pandemic. Our research makes three key contributions: (a) incorporating range volatility measures to capture the pandemic's impact on stock market volatility, (b) providing a comparative assessment of volatility spillover across sectoral indices, and (c) identifying evidence of volatility spillover across different sectoral indices. Using daily historical open, high, low, and close price data for 11 NIFTY sectoral indices during first wave of pandemic; the findings reveal that open-to-close returns outperform close-to-close returns in forecasting sectoral stock indices, underscoring the importance of incorporating range-based volatility measures in forecasting models. Furthermore, the multivariate Range DCC model confirms significant volatility spillover across sectoral indices, highlighting the interconnectedness of Indian sectoral stock markets during crisis periods. The findings offer actionable insights for the Securities and Exchange Board of India (SEBI) to develop targeted, sectoral-level market surveillance strategies and robust risk management frameworks, ultimately enhancing the resilience of India's capital markets in post-pandemic scenarios.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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The labour market participation of Moroccan women remains significantly lower than that of men. This issue has sparked academic and political debates in recent years. Numerous studies suggest solutions, including evaluating the effects of trade liberalization on female employment. Using a gender-sensitive computable general equilibrium (CGE) model calibrated to Morocco's 2019 Social Accounting Matrix (SAM), this study simulates the impact of trade liberalization on women's employment and wages.
The results of this study show that full national liberalization is pro-feminine in competitive and female labour-intensive sectors. However, it disadvantages women in less competitive and less female labour-intensive industries. This leads us to consider additional policies to stimulate women's employment in these latter sectors. Among these policies, the most effective are those targeting gender equality in social, legal, and financial aspects.
These policies yield favourable labour market outcomes: increased female labour supply in various sectors and a reduction in the gender wage gap. However, they have unfavourable macroeconomic consequences, such as production, exports, income, and investment declines. This is mainly due to the low productivity of Moroccan women compared to their male counterparts. This leads to the following conclusion: any policy aiming to increase women's participation in the labour market in Morocco must be accompanied by initiatives to improve their productivity.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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The study aims to understand the interconnectedness and interdependence of cryptocurrency with global uncertainties. The study employs quantile regression and Markov regime-switching models to understand the time-varying connectedness between the cryptocurrency market and uncertainties. The study findings reveal that geopolitical risk positively influences cryptocurrency returns at all quantiles, highlighting the significance of understanding geopolitical risk before considering the investments in cryptocurrency market. On the other hand, economic policy uncertainty negatively affects with the returns during economic expansions and at higher quantiles. Cryptocurrency market is independent of gold price volatility and oil price volatility significantly reduces cryptocurrency returns. The results suggest that cryptocurrency investments are attractive during geopolitical uncertainties, they are unfavourably affected by economic policy uncertainty and oil price volatility, reflecting complex investor behaviours.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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This paper explores the intersection of climate change, gender inequality, and human development through the lens of Amartya Sen’s Capability Approach, offering a comprehensive analysis of how these interconnected challenges exacerbate existing systemic inequalities. Climate change, as a multidimensional crisis, disproportionately impacts women, particularly in low- and middle-income countries, due to their limited access to resources, essential social services, and decision-making opportunities.
The study highlights how systemic patriarchal structures contribute to women’s increased vulnerability to environmental degradation, manifesting in challenges such as energy poverty, food insecurity, unpaid care work, and restricted agency. Using Sen’s Capability Approach, the paper argues for a shift in focus from mere resource allocation to enhancing individuals’ and community freedoms and opportunities, emphasising the need to develop capabilities that empower women and marginalised groups. This approach offers a deeper understanding of structural inequalities, illustrating how conversion factors - personal, social, and environmental - shape individuals’ abilities to achieve well-being and resilience in the face of climate challenges.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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This study examines the green finance strategies of G20 economies, with a particular focus on green bond issuance and its impact on greenhouse gas (GHG) emissions. Utilizing secondary data from the Statista database and the Emissions Database for Global Atmospheric Research, the analysis employs percentage evaluation, simple linear regression, heat maps, and cluster analysis. A Python-based algorithm in Jupyter Notebook facilitates the data processing.
Findings indicate that China leads in both green bond issuance and GHG emissions, followed by the United States. Regression analysis confirms that green bonds contribute to reducing GHG emissions. Notably, developed and developing countries exhibit similar patterns in green bond issuance and emissions, suggesting that these variables are not necessarily aligned with their respective development levels.
This research offers a wide assessment of the interplay between green bond issuance and environmental sustainability among G20 economies, highlighting the potential of green finance in fostering sustainable and inclusive growth. The findings provide insights into areas for improvement and policy recommendations for G20 nations to enhance their green financing strategies, increase green bond issuance, and reduce emissions in pursuit of global sustainability goals.
Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
