Financial Strategies Research Articles

Financing women entrepreneurship in the developing world: An fsQCA analysis of informal financing schemes

Financing women entrepreneurship in the developing world: An fsQCA analysis of informal financing schemes

Optimal decisions in a capital-constrained supply chain under credit guarantee and revenue-sharing contracts

This study proposes a novel Supply Chain Finance scheme (CG-RS), integrating credit guarantees (CG), and revenue-sharing contracts (RS). We analyze a three-party Stackelberg game between a capital-constrained retailer, supplier, and bank. We derived equilibrium strategies under both monopolistic and competitive bank markets to investigate the impact of the CG-RS scheme on the financial performance of each party. From the numerical analysis, we find that all parties can benefit from the CG-RS scheme by mitigating default risk and aligning incentives. The supplier's guarantee coefficient and the retailer's initial capital significantly influence the scheme's outcomes, while the revenue-sharing proportion plays a crucial role in balancing interests. Our findings suggest that the CG-RS scheme offers a promising solution for addressing capital constraints and enhancing supply chain performance.

Blinded by the ‘green-halo’? Equity in financing climate adaptation of urban sanitation

Adapting urban sanitation systems to changing climate conditions will require substantial investments. However, there is a gap in understanding the funding strategies for such adaptation measures, especially amid concerns that resilience measures might reinforce existing urban sanitation inequalities. Through cross-case document analysis and complementary key informant interviews, we examined the sanitation adaptation investments in eight cities, focusing on their funding arrangements and social and intergenerational equity implications. Debt financing of sanitation adaptation often relies on repayment through customer bills with only opaque considerations of the affordability for different socio-economic customer groups. The lack of appropriate accounting for the lifecycle costs of resilient infrastructure threatens to mortgage future generations. There is no convincing evidence that ‘greening’ of adaptation financing either shifts or redistributes the financial risk more equitably nor does it make the repayment of the investments substantially cheaper for customers. We conclude that a public sector funding approach is most appropriate to ensure social and intergenerational equity within climate-resilient sanitation systems.

Strategic Approaches to Municipal Bond Management in West Java’s Debt Management Unit

The research on the "Debt Management Unit (DMU) Strategy in the Issuance of Municipal Bonds in West Java Province" addresses critical challenges local governments face in optimising financial resources for infrastructure development. This study is significant within the realm of Social Sciences, especially public policy, as it explores innovative financial strategies that could potentially reshape regional economic development. The background of this study stems from the suboptimal performance of the DMU in managing municipal bonds, a crucial tool for financing public infrastructure. The research aims to analyze the role of the DMU, identify key factors influencing its effectiveness, and propose strategic solutions to enhance its performance. Using a qualitative methodology, the study gathers data through interviews, observations, and document analysis, providing a comprehensive understanding of the DMU's operational challenges. The findings contribute both academically and practically by highlighting the importance of strategic management in public finance, particularly in a developing region like West Java. The proposed strategies, developed through SWOT analysis, offer actionable insights for policymakers to optimize debt management, thus ensuring sustainable infrastructure development. This research not only enriches the academic discourse on public financial management but also provides practical solutions for local governments facing similar challenges, making it a valuable contribution to both scholarly and applied fields.

Pricing and Financing Strategies in a Dual-Channel Low-Carbon Supply Chain for Bilateral Capital-Constrained Retailers

In the context of low-carbon economy, financial constraints are prevalent among both upstream and downstream enterprises, exacerbated by the trend of online retailers extending into offline physical stores. This study first proposes and evaluates three financing combination models: “bank lending + advance payment,” (in short, AF), “bank lending + delayed payment,” (in short, DF), and “bilateral bank lending.” (in short, TF). It then examines the retailer’s dual-channel supply chain pricing and financing decision-making models under unified pricing strategy and independent self-pricing strategy respectively. The evaluation results indicate that among various financing models, the independent self-pricing strategy has the potential to enhance the retailer's profitability, outperforming the unified pricing strategy. In addition, when channel preference is significant, the retailer should prioritize the independent self-pricing strategy. A comparison of the optimal decisions across the three financing combination models indicates that both the cost coefficient of carbon emission reduction and the loan interest rate exert a limiting influence on the decision-making and income of all supply chain parties. Furthermore, the DF model emerges as a financing equilibrium strategy within the supply chain, with a shift to the AF model being considered only when the manufacturer’s loan interest rate is relatively high.

Household exposure to COVID‐19 and life insurance holdings

AbstractThe COVID‐19 pandemic has resulted in substantial loss of life and economic disruption, leading to a reevaluation of financial planning strategies. This paper examines the relationship between household exposure to COVID‐19 and their life insurance holdings using data from a nationwide household survey in the US. The results indicate that households diagnosed with COVID‐19 are more likely to own life insurance and hold a larger amount of coverage due to the pandemic, with these effects significantly influenced by the bequest motive. Conversely, hospitalization and the death of family members due to COVID‐19 are negatively correlated with life insurance holdings. The implications can inform household and industry decisions to enhance household financial welfare in response to pandemic risks.

Carbon emission reducing and financing strategies of capital-constrained supply chains using equity financing under different power structures

AbstractThis study considers a low-carbon supply chain model comprised of a capital-constrained manufacturer and a retailer under a cap-and-trade system. These two parties can choose from two financing modes: bank financing or mixed financing, in which the latter combines bank financing with equity financing. Two decision-making power structures are formulated, namely the Nash game model and the retailer-led Stackelberg game model. Subsequently, a comparative analysis of equilibrium outcomes for both parties within these power structures is performed, yielding the following insights: (1) In the bank financing mode, retailers find greater profitability in adopting dominance. When emission reduction efficiency is high, manufacturers obtain more profit within a Nash game context. However, when emission reduction efficiency is low, manufacturers accrue more profit in a Stackelberg game context. In the mixed financing mode without cap-and-trade regulations, supply chain participants do not express a preference for any specific game structure. (2) Without cap-and-trade regulation, in both the Nash and Stackelberg game models, the supply chain participants find more profitability in mixed financing when carbon emission reduction efficiency is high. Meanwhile, bank financing proves more profitable when the carbon emission reduction is low. Moreover, this study investigates the impact of wholesale and retail prices of low-carbon products on the financing strategies of the supply chain participants under different power structures and financing modes. Additionally, extended models are considered, including the manufacturer-led Stackelberg game structure and the capital-constrained manufacturer with a certain amount of initial capital.

Optimal Strategies for E-Commerce Platform Supply Chain: Carbon Emission Reduction and Financing

In the context of global carbon emission reduction (CER) targets and slowing economic growth, it is imperative for suppliers to make informed choices regarding CER and financing strategies. However, limited research has explored the impact of financing strategies on CER. This paper develops a supply chain model that includes a supplier, a manufacturer, an E-commerce platform (E-C platform), and consumers with a preference for low-carbon products. The supplier sets the wholesale price, while the manufacturer controls both the production quantity and the unit amount of CER. We examine whether the manufacturer will invest in CER with sufficient capital or under various financing scenarios, namely (1) traditional production with sufficient capital (Scenario ST); (2) CER implementation with sufficient capital (Scenario SG); (3) CER implementation with E-C platform financing (Scenario EG); (4) CER implementation with bank financing (Scenario BG). Through comparative analysis, the analysis reveals that, regardless of the financing method chosen, the supplier’s profit and the manufacturer’s production quantity increase when the manufacturer invests in CER technology innovation compared to the traditional scenario. Furthermore, in terms of the manufacturer’s profit, if the service cost of bank financing exceeds a certain threshold, the manufacturer should either seek financing from the E-C platform or abandon the CER investment. Additionally, with respect to CER outcomes, Scenario BG outperforms Scenario EG. These findings provide a theoretical foundation and decision-making support for supply chain participants when addressing carbon reduction and financing strategy decisions.

Using a policy learning lens to understand health financing policy outcomes: the case of translating strategic health purchasing into policy and practice in Burkina Faso

IntroductionAchieving universal health coverage (UHC) through an effective health financing system is a challenge for many low-income countries. Learning is key to success due to many uncertainties and unknowns. Using the case of translating strategic health purchasing into policy and practice in Burkina Faso, our study seeks to understand how policy learning can shape policy processes and outcomes.MethodsWe used a qualitative case study design and Dunlop and Radaelli’s conceptualisation of policy learning to identify which modes of learning did or did not occur, what helped or hindered them and the resulting policy outcomes. Dunlop and Radaelli frame policy learning as epistemic, reflexive, negotiative or hierarchical. We collected data through documentary review and in-depth individual interviews with 21 key informants. We analysed the data manually using pattern-matching techniques.ResultsThe introduction of strategic health purchasing in Burkina Faso was initially seen as an opportunity to reduce the fragmentation of the health financing system by coupling a performance-based financing scheme and a user fee exemption policy. However, this has faltered, and our findings suggest that an inability to harness all modes of learning has led to blockages. Indeed, while reflective learning was present, epistemic, hierarchical and learning through bargaining were absent, preventing national policy actors from defending their own policy or scheme from reaching compromises. But thanks to facilitating processes led by a well-resourced organisation and contextual elements that encouraged the emergence of more pluralistic modes of learning, some progress was achieved in operationalising strategic health purchasing.ConclusionsSome modes of learning seem to be overlooked in countries’ efforts to achieve UHC. Facilitation techniques and initiatives that encourage the use of all modes of learning, while supporting countries to take full ownership and responsibility for consolidating their own learning health systems, should be promoted.

Green bonds and clean energy stocks: Safe havens against global uncertainties? A wavelet quantile-based examination

The renewable energy sector has seen rapid investment growth over the last decade, with Green Bonds (GB) and Clean Energy Stocks (CLE) emerging as critical components of sustainable finance. Using daily data from 2013 to 2023, this study investigates the diverse roles of GB and CLE as potential safe havens under Geopolitical Risks (GEO) and Economic Policy Uncertainty (ECU). Applying an advanced Wavelet Quantile Regression (WQR) approach, we assess their effectiveness across different time horizons and risk conditions. The findings reveal that GB consistently offer stronger protection against economic risks, particularly in the short to mid-term, while CLE are more effective in shielding against geopolitical risks. Over longer periods, GB maintain their safe haven status, while CLE’s protective capabilities diminish. The results also highlight that GB and CLE behave differently across quantiles, with GB showing stability across extreme market conditions, making it a more reliable hedge overall. This study offers valuable policy insights for key stakeholders. Policymakers are encouraged to integrate GB and CLE into sustainable finance strategies, investors can mitigate risk by blending these assets, and portfolio managers should adjust allocations dynamically based on evolving market conditions. The results underscore the importance of green assets as hedges in times of global uncertainty, contributing to the growing literature on sustainable finance.

Tying energy resiliance and digital upgradation of technology transformation of electric power system: Does financial flexibility matters?

As the energy sector undergoes rapid technological transformation, the resilience of electric power systems has become a focal point in the context of climate change and energy security. This paper examines the interplay between energy resilience and the digital upgradation of electric power systems in China, with a particular emphasis on the role of financial flexibility. Using panel data from 2005 to 2023 across 30 provinces in China, we employ the Generalized Method of Moments (GMM) model to investigate how financial flexibility—measured through capital access and adaptive financial strategies—affects the integration of digital technologies such as smart grids, renewable energy systems, and advanced monitoring infrastructures. Our findings demonstrate that financial flexibility significantly enhances the resilience of electric power systems by facilitating quicker adoption of digital technologies and enabling systems to better withstand external shocks, such as fluctuating energy demands and environmental stressors. The study offers policy insights, underscoring the importance of supportive financial mechanisms to accelerate the digital transformation and sustainability of China's energy infrastructure. This research contributes to the ongoing discourse on energy resilience by highlighting the critical role of financial flexibility in enabling technological advancements within the power sector.

Collapse of Silicon Valley Bank: Impacts on Startups & Global Financial Markets, Comparison Between Lehman Brother and Silicon Valley Bank Crises

The sudden collapse of Silicon Valley Bank (SVB) sent shockwaves through the startup ecosystem and the global financial landscape. This research delves into the factors that led to SVB's downfall, exploring how its unique role in supporting tech startups became a double-edged sword. By examining the parallels between SVB's crisis and the infamous collapse of Lehman Brothers in 2008, we uncover important lessons about risk management and the fragility of financial institutions. The fallout from SVB's failure didn't just affect its immediate clients; it rippled through the entire venture capital community and beyond, prompting startups to rethink their banking relationships and funding strategies. We reflect on the broader implications for the financial industry and the lessons learned about the need for diversification and sustainable growth. Ultimately, this research is a reminder of the interconnectedness of our financial systems and the human stories behind the numbers. As we navigate an ever-changing economic landscape, the insights gained from SVB's collapse will shape the future of banking and entrepreneurship for years to come.

An investigation of regional public transport spend in Australia

This paper describes the outcome of a study to investigate the regional public transport spend in each state of Australia. The objective is to derive a reliable figure for comparison and the core metric used is spend per capita. The study assessed publicly available data on public transport spend, followed by a series of consultations with key staff from state bus associations and governments. The analysis was conducted separately for metropolitan (metro) and regional areas and then the regional data were further examined. While the Northern Territory (NT), Tasmania (TAS) and regional South Australia (SA) completely rely on bus services, Victoria (VIC) stands out as the state with the highest public transport spend per head in regional areas with robust train and bus networks which deliver the highest patronage. Western Australia (WA) is the state with the highest bus transport spend per head ensuring relatively high patronage despite the low population density. The study identifies low public transport patronage in regional areas as being influenced by low spending per capita and poor connectivity. It recommends prioritizing improvements in connectivity, multi-modal networks, and opening school buses to the general public. Additionally, funding strategies should focus more on patronage trends linked to spend rather than overemphasizing population density. Further, the study reveals that publicly available data seldom clearly segregates public transport spending in terms of regional and metro expenditure, which would lead to inefficiencies when assessing spatial equity in public transport provision.

An innovative method for short-term forecasting of blockchain cryptocurrency price

Cryptocurrency market sentiment is relatively unstable, which makes cryptocurrency price an attribute of high volatility. Accurate forecasting methods help to clarify the volatility trend of the cryptocurrency price, thereby reducing the investment risk of participants in the cryptocurrency market. Therefore, this research proposed a new method for short-term forecasting of the cryptocurrency price based on a small sample. This study took three typical blockchain cryptocurrencies (Bitcoin, Ethereum, Litecoin) as experimental objects, chose data intervals with different volatility trends in the U.S. stock indices between 2022 and 2023 as sample data, and used grey correlation analysis to select core affecting variables. Furthermore, this study built a grey multivariate convolution model with prioritized accumulating novel information for conducting prediction experiments on blockchain cryptocurrency price. The research findings demonstrate that the proposed model achieves high prediction accuracy in all experiments, and the model accuracy is superior to the comparison models. This study proposes a scientific prediction approach for blockchain cryptocurrency price, which can guide financial investors in developing and analyzing quantitative financial trading strategies to a certain extent. Meanwhile, this study provides a specific reference for relevant government departments to strengthen cryptocurrency regulation, prevent financial risks, and maintain financial stability.

Women’s Leadership in Sustainable Agriculture: Preserving Traditional Knowledge Through Home Gardens in Santa Maria Jacatepec, Oaxaca

Rural communities in Oaxaca face challenges such as food insecurity, economic instability, and the loss of traditional agricultural knowledge. Home gardens, once essential for food production and income generation, have declined due to migration, environmental degradation, and modern agricultural pressures. This study evaluated how community-driven models, supported by crowdfunding, can revitalize home gardens to improve food security, build community resilience, and promote economic empowerment. A mixed-methods approach, including household surveys, interviews, and focus groups, was used to assess the impact of these gardens. The results showed that over 70% of households experienced improved food security and reduced reliance on external food sources. Home garden production also led to a 20% reduction in food expenses, and the sale of surplus crops provided additional income, enhancing household economic stability. Women’s involvement in agricultural decision-making increased, fostering empowerment within both family and community contexts. Traditional practices, such as seed saving and organic pest control, were maintained, contributing to the ecological sustainability of the gardens. Crowdfunding was an effective tool for financing home gardens, boosting community engagement and strengthening social bonds. The findings highlight the potential of integrating traditional knowledge with modern funding strategies to create resilient, sustainable agricultural systems in rural Oaxaca.

AI-driven robo-advisors on personal financial management and investment strategies

The advent of AI-driven robo-advisors has revolutionized personal financial management and investment strategies. Leveraging sophisticated algorithms and machine learning, these platforms offer personalized financial advice and automated portfolio management with minimal human intervention. By analyzing vast datasets, including market trends, economic indicators, and individual client preferences, robo-advisors can create tailored investment plans that optimize returns and manage risks effectively. This paper explores the transformative impact of AI in democratizing financial advice, enhancing decision-making processes, and making investment management accessible to a broader audience. The integration of AI in robo-advisory services not only improves efficiency but also provides a scalable solution to meet the diverse financial needs of individuals.

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

Global prescriptions and local adaptations: the role of political culture in shaping governance strategies for university funding

ABSTRACT This article explores the influence of New Public Management (NPM) reforms on local higher education (HE) governance in the context of globalisation, focussing on the funding mechanisms of universities in Hong Kong and Taiwan. Through the development of an analytical framework for governance strategies, the study examines the principles underlying funding allocation and distribution decisions in these regions. The findings show that, although both regions adopt performance-based funding aligned with NPM principles, significant differences emerge due to their distinct political cultures and societal expectations. Taiwan’s strategy is characterised by regulatory oversight driven by societal shifts and electoral pressures, while Hong Kong’s approach is market-oriented, reflecting its British colonial heritage and alignment with industry demands. This study highlights the role of path dependency in shaping localised adaptations of global governance models, demonstrating the diversity and adaptability of HE governance strategies. The paper concludes by proposing directions for further research into the interplay between political culture and HE governance, advocating for the development of context-sensitive frameworks that address evolving governance strategies.

Exploring sustainable health financing strategies in humanitarian settings: Challenges, innovations and efforts towards Achieving Universal Health Coverage

Sustainable health financing is critical in humanitarian settings to ensure that the most vulnerable populations have access to necessary healthcare services. In crises such as conflicts, natural disasters, or prolonged emergencies, healthcare systems are often destabilized, leading to significant challenges in providing adequate care. This review explores innovative financing mechanisms designed to support health services in these contexts, focusing on their potential to achieve Universal Health Coverage (UHC). Key challenges include limited funding, high out-of-pocket costs, and the uneven distribution of healthcare resources. In response, emerging models such as impact bonds, Islamic social finance, disaster risk insurance, and digital financial solutions offer new approaches to mobilizing resources and sustaining health interventions. These strategies are essential to bridging gaps in healthcare access, promoting equity, and strengthening the resilience of health systems. Policy recommendations highlight the importance of integrating emergency and routine health services, engaging communities, and enhancing coordination among stakeholders. Strengthening health financing mechanisms is vital for long-term improvements in health outcomes, especially in fragile settings. This research underscores the need for sustainable, inclusive policies that bolster health systems in the face of humanitarian crises.

Predicting Gold Prices Through Machine Learning with Economic Indicators and Feature Selection Methods

The enormous fluctuation of the gold price in international markets gives predictive analysis an indispensable place for business firms, economists, and investors. With this in mind, this research aims to construct a machine learning model for gold price prediction based on past information and identify some leading patterns affecting the prices of such a commodity. The model thus covers a range of related financial and economic factors, be it past gold prices and inflation metrics, oil price vagaries, worldwide market indexes, and interest rate movements. Using machine learning algorithm approaches like Support Vector Machine (SVM), Random Forest, as well as Linear Regression approaches, the system is set to identify complex relationships prevailing in the data and provides predictions for short-term movements and long-term movements concerning price. The model will be trained and validated using historical data that includes the history of the gold price. In this respect, the findings of the study are relevant to stakeholders in financial forecasting who could make informed investment decisions. Additionally, the paper has outlined the method for feature selection that determines which factors are dominant in explaining the fluctuation of the gold price. The goal of the model, therefore, is to enhance its performance through prediction accuracy reduction in noise, recursive elimination of features, and application of correlation analysis. As a result, a robust evaluation of several machine algorithms, including support vector machines, allows the selection of a suitable model that best solves the prediction task. This approach to forecasting will therefore aid in the creation of instruments for risk management, automated trading techniques, and financial strategies for an individual as well as for organizations. Keywords: Support Vector Machine, Random Forest, machine learning, and gold price prediction; economic indicators, feature selection, and linear regression in the field of financial forecasting; erratic behaviour; and risk management.