Volume 13 | Issue 2 |

 Abstract

Indian investors have traditionally relied on fixed deposits (FDs) as a cornerstone of their investment portfolios due to the perceived safety and guaranteed returns these instruments offer. However, recent economic shifts--characterized by declining FD yields, evolving taxation policies, and enhanced market transparency--have paved the way for debt mutual funds (DMFs) to emerge as a compelling alternative. This research paper investigates the multifaceted reasons behind the increasing preference for DMFs over FDs, emphasizing the comparative advantages in terms of post-tax returns, liquidity, and the overall risk-return trade-off. Employing a robust mixed-method research design, the study integrates primary data obtained from a comprehensive survey of 500 investors--including retail investors, financial advisors, and institutional stakeholders--with qualitative insights drawn from detailed case studies. These case studies encapsulate individual portfolio rebalancing strategies and corporate treasury management practices, notably highlighting how institutions such as HDFC Bank have restructured their liquidity management through the adoption of DMFs. Complementary secondary data from regulatory reports, academic literature, and market performance analyses further contextualizes the findings. Empirical results indicate that nearly 45% of surveyed investors have partially shifted their investments from FDs to DMFs in the past five years. The transition is largely attributed to the enhanced tax efficiency provided by DMFs--primarily due to the benefits of indexation on long-term capital gains--as well as their superior liquidity and flexibility. Despite the moderate risks associated with market volatility and credit defaults inherent in DMFs, the potential for higher inflation-adjusted returns appears to outweigh these concerns for a significant segment of investors. The study concludes by offering targeted policy recommendations aimed at further enhancing investor literacy, reinforcing credit risk management protocols, improving market liquidity mechanisms, and revisiting taxation frameworks. Collectively, these measures are expected to support a more balanced and resilient fixed-income market in India, where a diversified portfolio incorporating both FDs and DMFs can achieve an optimal risk-return equilibrium in a rapidly evolving financial landscape.

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 Keywords

Debt Mutual Funds, Fixed Deposits, Tax Efficiency, Investor Behaviour, Risk-Return Trade-off, India

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This project addresses critical water quality issues by developing a system for real-time waterquality monitoring and ground-level water analysis. Traditional water monitoring approacheshave limitations, such as delayed data collection and limited data accuracy, which impedetimely pollution detection and response. This study proposes an Internet of Things (IoT)-based solution to bridge this gap, focusing on creating a sensor network that enables continuous monitoring of water quality in industrial and domestic water sources. Utilizing devices like the Arduino Uno, ESP8266, and various sensors--including those for temperature, pH, turbidity,Total Dissolved Solids (TDS), and conductivity--the system gathers data on essential waterquality parameters. This data is then processed and visualized using the ThingSpeak platform, allowing real-time updates accessible to stakeholders.The system's core methodology includes sensor data acquisition, preprocessing, and analysis via machine learning models to assess water quality trends and predict pollution risks. With a focus on adaptability, the system supports varied environmental contexts, enhancing its utilityacross different water bodies. This research contributes significantly to sustainable water management, especially in pollution-prone industrial zones, by offering a scalable, real-time solution for continuous water quality assessment. The implementation of this system has the potential to inform and improve water resource policies, reinforcing environmental and publichealth protection.

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 Keywords

1.Water Quality Monitoring 2.Groundwater Level Prediction 3.Machine Learning 4.Environmental Monitoring 5.Water Contamination 6.Data-driven Prediction 7.Groundwater Management 8.Artificial Intelligence (AI)

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The stock market is a crucial component of a country's financial system, servicing as indicator of that country's economic health. While stock market is influenced by various external and internal factor, this study examines the performance of six NSE sectoral indices on selected macroeconomic variables including GDP, gold prices, exchange rates, repo and reverse repo rates. The study uses secondary data from 2020-2024 and a descriptive methodology. The associations between macroeconomic factors and stock market performance are investigated using statistical techniques, such as descriptive statistics, the Granger Causality Test, the Augmented Dickey-Fuller (ADF) test, correlation analysis, Johansen's Cointegration Test, and regression analysis. The results show strong interdependencies, with GDP and gold prices having a positive impact on stock indices and interest rates and exchange rates having varying effects on different sectoral indices. The study helps investors, policy makers, and financial analysts make better investment choices, manage risk, and formulate economic policies by offering insightful information.

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Macro-economic variables, NSE, Nifty Sectoral indices, GDP, Gold prices, Exchange rates, Repo Rate, Reverse repo rates.

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This study examines the impact of AI-powered financial tools on investment decisions in the IT sector, focusing on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). By leveraging statistical analyses such as Descriptive statistics and ANOVA models the research explores how socioeconomic factors, financial literacy, and risk tolerance influence AI adoption among investors. Findings indicate that younger investors (18-25) are more likely to use AI tools, while factors like occupation and education show no significant impact. Additionally, financial literacy levels vary, highlighting the need for targeted investor education. The study also finds that AI usage does not significantly influence risk preferences, suggesting its potential in reducing investment biases. The research underscores the importance of improving AI accessibility and financial education to enhance investor decision-making. The findings provide valuable insights for financial institutions, policymakers, and AI developers to optimize AI-driven investment strategies in the evolving stock market landscape.

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Financial Literacy, AI-Powered financial tools, NSE, BSE, Risk tolerance, IT Sector.

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ESG (Environmental, Social, and Governance) factors have been widely recognized as key indicators of corporate sustainability. This study looks at the relationship between a firm's financial performance and its ESG score, drawing insights from stakeholder theory. The sample includes 41 Indian financial institutions, with data for the fiscal years 2022 and 2023 sourced from CRISIL. To analyze the impact of ESG scores on return on equity (ROE), return on assets (ROA), and market performance, the study employs linear regression and correlation analysis. The findings align with stakeholder theory, indicating a positive correlation between a firm's financial performance and its ESG score. To further extend the analysis, the study incorporates beta value calculations for the fiscal years 2022-2023 to assess market volatility and risk exposure. Additionally, a time series analysis is conducted to evaluate trends and patterns in ESG scores and financial performance over time. This research contributes to the restricted writing on Indian financial firms by investigating the significance of ESG within the monetary segment and highlighting its part in driving productivity and sustainable business practices.

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ESG (environmental, social and governance), Indian Financial Institutions, Sustainability, Corporate Governance, Risk Analysis, time series, ESG disclosure.

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 Abstract

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A substantial percentage of deaths in individuals with chronic kidney disease (CKD) are attributable to cardiovascular disease (CVD), which is the primary cause of morbidity and mortality in this population. Endothelial dysfunction, oxidative stress, chronic inflammation, and an elevated prothrombotic state are some of the intricate processes that interact between CKD and CVD. These factors increase the likelihood of arterial calcification and atherosclerotic events, which are exacerbated by conventional CVD risk factors such diabetes, hypertension, and dyslipidemia, which are commonly seen in individuals with chronic kidney disease. For patients with established atherosclerotic disease and those at high cardiovascular risk, antiplatelet therapy--specifically, aspirin and clopidogrel--is essential for lowering thrombotic consequences. Aspirin, an irreversible inhibitor of cyclooxygenase-1 (COX-1), reduces platelet aggregation by blocking the formation of thromboxane A2. Clopidogrel, an inhibitor of the P2Y12 receptor, stops platelets from activating and aggregating when ADP is present. Both medications have shown effective in the general population, but because of their changed pharmacokinetics, elevated risk of bleeding, and greater vulnerability to side effects, using them in CKD patients poses special difficulties[1]

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 Keywords

Chronic kidney disease, cardiovascular disease, antiplatelet therapy, aspirin, clopidogrel, thrombosis, bleeding risk, uremia, platelet dysfunction, secondary prevention, gastrointestinal bleeding, pharmacokinetics, P2Y12 inhibitors, COX-1 inhibitors, hemodialysis

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The Stock Market has emerged as a crucial component of the global financial system, facilitating investment and economic growth. In India, the National Stock Exchange (NSE) plays a pivotal role in ensuring market efficiency, transparency, and liquidity. As financial markets continue to evolve, understanding these developments is essential for investors to navigate volatility effectively and maximize returns. The paper examines the nature of stock volatility and clustering, volatility patterns in financial time series data and leverage or asymmetric effect exists in selected stocks. The study indicates that volatility in selected stocks, demonstrates characteristics such as volatility clustering, the asymmetry effect, and the persistence of volatility in daily returns. It highlights that both recent and past news influence market volatility. Additionally, the study identifies the presence of a leverage effect, suggesting that negative shocks or unfavorable news have a greater impact on volatility compared to positive shocks or favorable news.

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Stock volatility, Volatility Patterns, Stock Return, Leverage Effect

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Child mental health is becoming a significant global concern due to increasing prevalence of mental health conditions and limited access to specialized care. The telepsychiatry approach, which uses telecommunication technologies to provide psychiatric services, has emerged as a promising solution to bridge this gap. This review explores the role of telepsychiatry in monitoring and managing child mental health. It discusses the current technologies, including video consultations, mobile applications, and wearable devices, along with their effectiveness in diagnosing, treating, and supporting children with mental health issues. The paper highlights the advancements in telepsychiatry, including AI-driven diagnostic tools and remote therapy platforms, while discussing barriers such as digital access, clinician training, and privacy concerns. This review, based on analyzing recent studies and case examples, underscores the potential that telepsychiatry brings to revolutionize child mental health care to become more accessible, cost-effective, and individualized. It leads to a discussion of future research directions and policy considerations that guarantee equitable and sustainable implementation in different populations.

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Child Mental Health ,Telepsychiatry ,Remote Mental Health Monitoring ,Parent-Child Interventions via Telepsychiatry

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