This study investigates the asymmetric impact of digital financial inclusion (DFI) on economic growth across a panel of developing economies. Utilizing quantile regression, we analyze the heterogeneous effects of DFI across different quantiles of the economic growth distribution, offering a more nuanced understanding than traditional mean-based regression techniques. Our findings reveal that the impact of DFI is significantly more pronounced in countries experiencing lower economic growth, suggesting its potential as a catalyst for accelerating development in less prosperous regions. We also explore the role of specific DFI indicators, such as mobile banking penetration and FinTech adoption, in driving these asymmetric effects. The study contributes to the growing body of literature on the nexus between financial inclusion and economic development, providing valuable insights for policymakers seeking to leverage digital technologies to promote inclusive and sustainable growth. Furthermore, the study highlights the importance of tailored policies to address the specific needs and challenges of different developing economies in maximizing the benefits of DFI.
This research paper delves into the multifaceted impact of algorithmic trading (AT) on market dynamics within emerging economies, with a particular focus on India. The study examines the influence of AT on market efficiency, volatility, and the evolving regulatory landscape. Through a rigorous literature review and quantitative analysis, we assess the benefits and risks associated with AT adoption, including its potential to enhance liquidity, reduce transaction costs, and contribute to price discovery, while also addressing concerns about increased volatility, market manipulation, and systemic risk. The research also critically evaluates existing regulatory frameworks and proposes recommendations for adapting these frameworks to effectively govern AT activities in emerging markets, fostering innovation while mitigating potential adverse consequences. The findings contribute to a deeper understanding of the complex interplay between technology, finance, and regulation in shaping the future of financial markets in the developing world.
Emerging markets present both significant opportunities and considerable challenges for investors. High growth potential is often coupled with increased volatility and uncertainty, demanding sophisticated forecasting models for informed investment decisions. This paper proposes a novel hybrid forecasting model that integrates the strengths of traditional econometric techniques, specifically Generalized Autoregressive Conditional Heteroskedasticity (GARCH), with machine learning algorithms, such as Random Forests, to predict asset returns in emerging markets. By combining GARCH's ability to capture volatility clustering with Random Forests' capacity for non-linear pattern recognition, the hybrid model aims to enhance forecasting accuracy and improve risk management. The model's performance is evaluated using historical data from a selection of emerging market indices, demonstrating its superior predictive power compared to benchmark models. The findings provide valuable insights for investors seeking to navigate the complexities of emerging market investments and make more strategic, data-driven decisions.
This paper investigates the impact of digital financial inclusion (DFI) on sustainable economic growth in emerging economies. Utilizing a panel dataset of 25 emerging economies from 2010 to 2022, we employ a Generalized Method of Moments (GMM) estimation technique to address potential endogeneity issues. Our analysis focuses on various dimensions of DFI, including mobile money penetration, internet access, and the use of fintech services. The results demonstrate a significant positive relationship between DFI and sustainable economic growth, measured by GDP growth rate adjusted for environmental degradation. We find that increased access to digital financial services empowers marginalized populations, fosters entrepreneurship, and enhances overall economic efficiency, leading to more sustainable growth trajectories. Furthermore, the study examines the moderating role of institutional quality and human capital in amplifying the impact of DFI on economic growth. The findings provide valuable insights for policymakers in emerging economies seeking to leverage DFI as a catalyst for inclusive and sustainable development.
This study investigates the impact of dynamic capabilities on the performance of Polish manufacturing Small and Medium Enterprises (SMEs) within the context of ongoing digital transformation. We examine the mediating role of resource orchestration and absorptive capacity in translating dynamic capabilities into improved firm performance. Employing a longitudinal research design and analyzing data collected from a panel of Polish manufacturing SMEs over a five-year period, our findings reveal a significant positive relationship between dynamic capabilities and firm performance. Furthermore, we find that resource orchestration and absorptive capacity partially mediate this relationship, highlighting their importance in effectively deploying and leveraging internal and external resources for competitive advantage. The study contributes to the dynamic capabilities literature by providing empirical evidence from a transitional economy context and offering practical insights for SMEs navigating the challenges and opportunities of digital transformation.
This paper investigates the complex relationship between behavioral biases exhibited by human traders and the increasing prevalence of algorithmic trading systems in financial markets. Utilizing an agent-based modeling (ABM) framework, we simulate a market environment populated by both behavioral and algorithmic agents. Behavioral agents are endowed with cognitive biases, such as loss aversion, herding, and anchoring, while algorithmic agents are programmed with rational strategies and high-frequency trading capabilities. The simulation results demonstrate that the interaction between these two agent types significantly impacts market efficiency, volatility, and price discovery. Specifically, we find that algorithmic trading can exacerbate the effects of behavioral biases, leading to increased market instability, but also possesses the potential to mitigate these biases under certain market conditions. This research contributes to a deeper understanding of the evolving dynamics of financial markets in the age of algorithmic dominance and provides insights for policymakers and market participants seeking to enhance market stability and efficiency.