Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantum algorithms could play a transformative role in enhancing portfolio management strategies. The proposed Quantum Support Vector Machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantum algorithms, including Quantum Principal Component Analysis (QPCA), Quantum Boltzmann Machines (QBM), and Quantum K-Means Clustering (QKC), by an impressive margin of 25.15%.

This research highlights the crucial role that the economic empowerment of women plays in promoting gender equality and overall well-being. Women's empowerment has become a significant topic in development economics and modernization theory. Empowering women can lead to an improvement in the quality of human resources available for economic development. Both governmental and non-governmental initiatives, such as microfinance, job training, and mentoring, can improve access to credit, job prospects, and economic security. Tailored financial education is essential, particularly in India, given the disparities. Strategic government interventions, accessible banking services, and targeted financial education are crucial for empowering women and advancing their economic independence. The study aims to provide evidence of the impact of social and economic factors on women's empowerment through financial inclusion in India. Request access from your librarian to read this chapter's full text.

- Capital structure decisions play a pivotal role in determining corporate financial stability, risk profile, and overall valuation. This study evaluates the determinants influencing capital structure choices—specifically comparing Debt versus Equity financing—across high-growth firms and mature, stable firms. With a focus on financial flexibility, bankruptcy risk, tax shield benefits, information asymmetry, and agency costs, a descriptive research design was adopted. Primary and secondary data were evaluated across 150 corporate financial profiles using structured financial metrics and standardized survey assessments on a five-point Likert scale. The findings reveal that high-growth firms exhibit a significant preference for equity financing driven by financial flexibility, volatile cash flows, and high growth opportunities, whereas stable firms rely heavily on debt financing to leverage interest tax shields and optimize cost of capital. The study offers practical insights for corporate financial strategists and investment bankers to align capital architecture with organizational maturity and risk tolerance. Key Words: Capital Structure, Debt vs. Equity, High-Growth Firms, Stable Firms, Financial Flexibility, Leverage, Trade-Off Theory, Pecking Order Theory.

In response to the pressing challenges of climate change and biodiversity decline, recent attention has focused on leveraging technological solutions to protect the planet for future generations. The implementation of technology has proven to be instrumental in addressing environmental issues, ranging from renewable energy solutions to sustainable agriculture practices. Among these technological solutions, blockchain has emerged as a promising solution to combat the climate crisis. By utilizing a distributed and decentralized ledger, blockchain enables the monitoring and verification of transactions securely and transparently, without the need for intermediaries. Originally associated with cryptocurrencies, researchers now recognize its potential in tackling climate change beyond financial transactions, encompassing product sustainability tracking and pollution level monitoring. The evolution of distributed ledger technologies (DLT) since the advent of blockchain presents transformative opportunities across social and economic contexts, particularly gaining momentum in the post-COVID-19 economic landscape. Blockchain’s intrinsic qualities, including transparency, immutability, and durability, make it a transformative force in business sustainability. This chapter explores the convergence of blockchain technology and business sustainability, highlighting opportunities and challenges. It explores blockchain technology’s fundamental principles and applications before examining the concept of business sustainability, emphasizing the need for organizations to balance economic growth with social and environmental responsibility. <!----> <!----> <!----> <!---->