This paper investigates the impact of algorithmic trading (AT) on market efficiency and price discovery within the Indian stock market. Utilizing high-frequency data from the National Stock Exchange (NSE) over a period of five years, we employ econometric techniques, including event study methodology and vector autoregression (VAR) models, to analyze the effects of increased AT activity on various market microstructure characteristics. Our findings suggest that while AT can enhance liquidity and speed up price discovery, it also contributes to increased volatility and the potential for market manipulation. The results highlight the complex relationship between AT and market quality, providing valuable insights for policymakers and market participants seeking to optimize the benefits and mitigate the risks associated with this evolving trading paradigm. Further research is needed to explore the long-term implications of AT and develop effective regulatory frameworks to ensure fair and efficient market operations.
This research paper investigates the multifaceted relationship between fintech adoption, financial inclusion, and economic resilience in emerging economies. Utilizing a cross-sectional analysis of data from a diverse set of emerging nations, we examine the extent to which the proliferation of fintech solutions contributes to increased financial inclusion and, subsequently, enhances economic resilience in the face of external shocks. The study employs a robust econometric framework, incorporating instrumental variables and control variables, to address potential endogeneity issues and isolate the causal impact of fintech adoption. Our findings reveal a significant positive correlation between fintech adoption and financial inclusion, particularly among previously underserved populations. Furthermore, we demonstrate that enhanced financial inclusion, facilitated by fintech, strengthens economic resilience by promoting diversification of financial assets, improving access to credit, and fostering greater economic participation. The paper concludes with policy recommendations aimed at fostering a conducive environment for fintech innovation, promoting responsible financial inclusion, and bolstering the resilience of emerging economies in an increasingly volatile global landscape.
This paper investigates the impact of algorithmic trading (AT) on market efficiency and price discovery in emerging economies. We analyze intraday trading data from a select emerging market stock exchange to assess the effects of AT on various market microstructure measures, including price volatility, liquidity, order imbalance, and price discovery contributions. Our methodology involves a combination of event study analysis and regression models to isolate the effects of AT adoption and intensity. The results indicate that while AT generally improves liquidity and price discovery, it can also contribute to increased volatility, particularly during periods of high market stress. Furthermore, the impact of AT is contingent on the regulatory environment and the level of technological infrastructure in each specific emerging market. We conclude with policy recommendations aimed at maximizing the benefits of AT while mitigating its potential risks.
This paper investigates the impact of algorithmic trading (AT) on market efficiency and price discovery in the Indian stock market, an emerging economy context. We employ econometric techniques to analyze high-frequency data from the National Stock Exchange (NSE) to assess the relationship between AT activity, market liquidity, price volatility, and information dissemination. Our findings suggest that while AT can enhance liquidity and contribute to faster price discovery under certain conditions, it can also exacerbate volatility and increase information asymmetry, particularly during periods of market stress. The study contributes to the ongoing debate surrounding the role of AT in financial markets and provides valuable insights for policymakers and regulators in emerging economies seeking to harness the benefits of technological advancements while mitigating potential risks. The research goes beyond previous studies by examining the nuanced effects of various AT strategies and their interplay with market microstructure features specific to the Indian context.
This research paper investigates the impact of Fintech adoption on financial inclusion and the growth of Small and Medium-sized Enterprises (SMEs) in emerging economies, focusing specifically on India. Utilizing a mixed-methods approach, combining quantitative analysis through logistic regression with qualitative insights from case studies, the study examines the relationship between Fintech usage, access to financial services, and subsequent SME performance indicators such as revenue growth, profitability, and job creation. The findings reveal a significant positive correlation between Fintech adoption and financial inclusion, leading to enhanced access to credit, payment solutions, and other financial services for SMEs. Furthermore, the study demonstrates that Fintech adoption is associated with improved SME growth, particularly in sectors with high digital penetration. The paper concludes with policy recommendations aimed at fostering a supportive ecosystem for Fintech innovation and promoting its adoption among SMEs to accelerate financial inclusion and sustainable economic development.