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VOLUME 3, ISSUE 1, JANUARY 2026
The Efficiency of Shriram Mutual Funds: A Risk-Adjusted Return Perspective
MENDEM VARUN TEJ, UNDABATLA RAMBABU
Sustainable Finance and Green Accounting: A Study on Integrating Environmental Performance Metrics into Corporate Reporting
Kavali Rudransh*, R. Kranthi
An Empirical Study on Risk–Return Performance of Small-Cap Mutual Funds in India
Dr. Undabatla Rambabu, Kodati. Meghana
A Comparative Analysis of Risk-Adjusted Returns in ELSS Tax-Saving Funds
YARABOLU AKHILA, UNDABATLA RAMBABU
Forecasting Risk-Adjusted Performance Metrics Using ARIMA Models: Evidence from Sharpe, Jensen, and Treynor Ratios
DACHEPALLI PURUSHOTHAM, MENDEM VARUN TEJ, Dr. NARESH OGIRALA, SHAIK TASNEEM TABASSUM, TUMMURU KRISHNA CHAITHANYA REDDY
Self-Help Groups and RurWomen Empowerment Through Poverty Alleviation: Problems and Prospects
Krishna Saha
Machine Learning Models and Interactive Dashboards in Breast Cancer Detection
Dr. Emmanuel Udoh, Dr. Tachi Udoh, Liz Udoh
Optimized Deep Learning Model for Accurate Detection of Liver Diseases Using Ultrasound Imaging: A Case Study
A. Sahaya Mercy, Dr. G. Arockia Sahaya Sheela
Embedding Human Oversight in Semi-Automated Decision Structures for Critical Applications
Ajay Kumar Suwalka, Nirmal Singh*, Awanit Kumar
Artificial Intelligence in Insurance: Transforming Risk Assessment and Claims Management
K. Pandieswari*, U. Arumugam
A Multi-Metric Performance Evaluation of Indian Mutual Funds: Evidence from Arbitrage, Hybrid, and Balanced Advantage Schemes
MENDEM VARUN TEJ, DACHEPALLI PURUSHOTHAM, UNDABATLA RAMBABU, SHAIK TASNEEM TABASSUM,KONDIBOYINA PAVAN SAI
Abstract
The Efficiency of Shriram Mutual Funds: A Risk-Adjusted Return Perspective
MENDEM VARUN TEJ, UNDABATLA RAMBABU
DOI: 10.17148/IMRJR.2026.030101
Abstract: This study looks at how Shriram Mutual Fund schemes performed on a risk-adjusted basis between 2020 and 2025, comparing them with their benchmark indices. Using annual returns, volatility, and Sharpe Ratios based on the 364-day T-bill rate, the analysis covers 16 plans across Aggressive Hybrid, Balanced Advantage, ELSS, and Flexi Cap categories. Overall, Direct plans delivered better results than Regular plans, with Flexi Cap and ELSS Direct schemes standing out. Hybrid funds—especially Aggressive Hybrid—showed weaker performance and did not compensate well for the risks taken. The findings underline that Direct equity-oriented plans tend to offer better efficiency and that metrics like the Sharpe Ratio are essential for evaluating mutual fund choices in India.
Keywords: Mutual Fund Performance, Risk-Adjusted Returns, Sharpe Ratio, Benchmark Comparison, Direct vs Regular Plans, Portfolio Optimization, Quantitative Analysis, Shriram Mutual Fund, Financial Metrics, Investment Decision- Making
Keywords: Mutual Fund Performance, Risk-Adjusted Returns, Sharpe Ratio, Benchmark Comparison, Direct vs Regular Plans, Portfolio Optimization, Quantitative Analysis, Shriram Mutual Fund, Financial Metrics, Investment Decision- Making
Abstract
Sustainable Finance and Green Accounting: A Study on Integrating Environmental Performance Metrics into Corporate Reporting
Kavali Rudransh*, R. Kranthi
DOI: 10.17148/IMRJR.2026.030102
Abstract: The global shift toward sustainability has reshaped how corporations evaluate and disclose their environmental responsibilities. Sustainable finance and green accounting have emerged as two central pillars that guide organizations toward long-term ecological stewardship, resource efficiency, and transparent reporting. This research examines how environmental performance metrics such as carbon emissions, renewable energy consumption, water usage, waste management, and ecological efficiency are incorporated into corporate reporting. Drawing on contemporary literature, international sustainability frameworks, and an illustrative dataset, the study explores the role of green accounting in strengthening environmental performance assessment, improving transparency, and supporting sustainable investment decisions. The findings reveal that although companies increasingly recognize the importance of environmental reporting, challenges persist in standardization, data credibility, measurement accuracy, and cross-country regulatory variation. The study concludes that stronger regulatory mandates, widespread adoption of global sustainability frameworks, digital ESG reporting tools, and environmentally aligned financial instruments are essential for accelerating the integration of environmental metrics into corporate reporting.
Keywords: sustainable finance, green accounting, environmental metrics, corporate reporting, ESG disclosure, carbon emissions, sustainability frameworks, integrated reporting
Keywords: sustainable finance, green accounting, environmental metrics, corporate reporting, ESG disclosure, carbon emissions, sustainability frameworks, integrated reporting
Abstract
An Empirical Study on Risk–Return Performance of Small-Cap Mutual Funds in India
Dr. Undabatla Rambabu, Kodati. Meghana
DOI: 10.17148/IMRJR.2026.030103
Abstract: Small-cap mutual funds are emerging as the most credible means for almost all investors to learn capital appreciation in the long run through investments in small-cap emerging companies. These schemes typically invest in companies that are smaller in market capitalization and that offer lower growth but come with a higher risk and volatility. In India, in recent years, small-cap funds have stirred up a lot of attention among investors because of their delivery during favourable market conditions.
The increasing and effective participation of retail investors, in addition to online-based infrastructure for investments, has played a great role in expansion of small-cap MF investments in the Indian Market. However, performances of schemes tend to vary widely owing to the numerous, ever-changing circumstances of the markets and portfolio strategies as well as fund management methods. Thus, a careful analysis is needed to understand how these schemes perform when compared with broad and reliable benchmark indices such as the BSE SC 250 and NIFTY SC 250.
This study focuses on analyzing the risk–return performance of selected small-cap mutual fund schemes using quantitative tools. Measures such as Avg return, Stand devia, beta, SR, TR, J’s Alpha are used to assess both absolute and risk-adjusted performance. Together, these indicators provide a clear picture of how efficiently the funds perform and how they respond to market fluctuations.
This study's findings will help investors identify small-cap mutual fund schemes that perform better, and it will inform them to take enlightened investment decisions through objective analysis of performance.
Keywords: Small-Cap Mutual Funds, Risk-Return Analysis, Fund Performance, and Benchmark Indices SR, TR, J's Alpha, Investment Evaluation will be the keywords.
The increasing and effective participation of retail investors, in addition to online-based infrastructure for investments, has played a great role in expansion of small-cap MF investments in the Indian Market. However, performances of schemes tend to vary widely owing to the numerous, ever-changing circumstances of the markets and portfolio strategies as well as fund management methods. Thus, a careful analysis is needed to understand how these schemes perform when compared with broad and reliable benchmark indices such as the BSE SC 250 and NIFTY SC 250.
This study focuses on analyzing the risk–return performance of selected small-cap mutual fund schemes using quantitative tools. Measures such as Avg return, Stand devia, beta, SR, TR, J’s Alpha are used to assess both absolute and risk-adjusted performance. Together, these indicators provide a clear picture of how efficiently the funds perform and how they respond to market fluctuations.
This study's findings will help investors identify small-cap mutual fund schemes that perform better, and it will inform them to take enlightened investment decisions through objective analysis of performance.
Keywords: Small-Cap Mutual Funds, Risk-Return Analysis, Fund Performance, and Benchmark Indices SR, TR, J's Alpha, Investment Evaluation will be the keywords.
Abstract
A Comparative Analysis of Risk-Adjusted Returns in ELSS Tax-Saving Funds
YARABOLU AKHILA, UNDABATLA RAMBABU
DOI: 10.17148/IMRJR.2026.030104
Abstract: This study analyses the performance of the ICICI Prudential ELSS Tax Saver Fund by comparing its Net Asset Value (NAV) with the NIFTY 100 market index over a five-year period from April 2020 to March 2025. This study aims to examine how the chosen mutual fund has performed by looking at its returns and the level of risk involved, and then comparing these results with the overall stock market. The data used for the analysis is secondary in nature and has been taken from historical NAV and index price records. The study evaluates year-wise changes in fund value and market movement to identify growth trends and volatility. The findings of the study help investors understand whether the ELSS fund has delivered consistent performance and whether it is suitable for long-term tax-saving and wealth creation purposes. Overall, the study provides a clear picture of the fund’s stability, growth potential, and performance in comparison with the market index.
Keywords: ICICI Prudential ELSS Fund, NIFTY 100 Index, Mutual Fund Performance, Net Asset Value (NAV), Risk and Return, Tax-Saving Investment, Long-Term Investment, Market Comparison, Portfolio Growth, Volatility Analysis.
Keywords: ICICI Prudential ELSS Fund, NIFTY 100 Index, Mutual Fund Performance, Net Asset Value (NAV), Risk and Return, Tax-Saving Investment, Long-Term Investment, Market Comparison, Portfolio Growth, Volatility Analysis.
Abstract
Forecasting Risk-Adjusted Performance Metrics Using ARIMA Models: Evidence from Sharpe, Jensen, and Treynor Ratios
DACHEPALLI PURUSHOTHAM, MENDEM VARUN TEJ, Dr. NARESH OGIRALA, SHAIK TASNEEM TABASSUM, TUMMURU KRISHNA CHAITHANYA REDDY
DOI: 10.17148/IMRJR.2026.030105
Abstract: This study uses ARIMA time series models to examine and predict three important measures of investment performance: the Sharpe Ratio, Jensen Ratio, and Treynor Ratio. Autocorrelation (ACF) and partial autocorrelation (PACF) plots are analyzed for each metric to help select appropriate models. Forecasts are produced starting from the 13th time period, along with 95% confidence intervals. The results show that forecasts for all three ratios remain close to zero and are accompanied by relatively narrow confidence ranges, indicating minimal short-term fluctuation. Among the three measures, the Treynor Ratio displays slightly lower forecast uncertainty, reflected in smaller standard errors. Overall, the findings suggest stable performance expectations, which can aid investment evaluation and risk assessment when market conditions remain steady.
Keywords: ARIMA models, Sharpe ratio, Jensen ratio, Treynor ratio, time series forecasting, autocorrelation (ACF), partial autocorrelation (PACF), financial performance metrics, investment risk analysis, and confidence intervals.
Keywords: ARIMA models, Sharpe ratio, Jensen ratio, Treynor ratio, time series forecasting, autocorrelation (ACF), partial autocorrelation (PACF), financial performance metrics, investment risk analysis, and confidence intervals.
Abstract
Self-Help Groups and RurWomen Empowerment Through Poverty Alleviation: Problems and Prospects
Krishna Saha
DOI: 10.17148/IMRJR.2026.030106
Abstract: Women empowerment is a big issue today and it is relevant to the development of any nation or country. To ensure women empowerment, the country has chosen several paths, the Self-Help Group is one of them and associated with income generation and poverty alleviation through women empowerment of rural areas. The study will show the working procedures of Self-Help Groups and obstacles towards its success. This paper will show some remedial ways to prevent existing obstacles.
Keywords: Women empowerment, rural women, SHGs, poverty alleviation
Keywords: Women empowerment, rural women, SHGs, poverty alleviation
Abstract
Machine Learning Models and Interactive Dashboards in Breast Cancer Detection
Dr. Emmanuel Udoh, Dr. Tachi Udoh, Liz Udoh
DOI: 10.17148/IMRJR.2026.030107
Abstract: Breast cancer is one of the leading types of cancer in the world, affecting many people every year. Early diagnosis with high accuracy is vital for treatment and patient care. In this project, publicly available cancer data sets from the University of California, Irvine Repository (kaggle.com) were analysed. Data analysis reveals that cancer morphological features, such as radius, perimeter, and area, exhibit a very high correlation coefficient in the detection process. The decision tree model revealed that the concave point is a highly relevant predictor, with a threshold of 0.048 to distinguish between malignant and benign tumors. The logistic regression model achieved an accuracy of 80.95% and an F1 score of 0.75, indicating good overall classification performance; however, a precision score of 0.60 suggests a moderate capability to minimize false predictions. By leveraging machine learning models and interactive dashboards (utilizing advanced data analytics and visualization), the work supports healthcare professionals in making more informed decisions regarding tumor classification and patient care.
Keywords: Breast Cancer, Interactive Dashboard, Machine Learning, Visualization Tool, Detection/Risk Management
Keywords: Breast Cancer, Interactive Dashboard, Machine Learning, Visualization Tool, Detection/Risk Management
Abstract
Optimized Deep Learning Model for Accurate Detection of Liver Diseases Using Ultrasound Imaging: A Case Study
A. Sahaya Mercy, Dr. G. Arockia Sahaya Sheela
DOI: 10.17148/IMRJR.2026.030108
Abstract: This study presents an optimized deep learning model designed to enhance the accuracy of liver disease detection using ultrasound imaging. Ultrasound images often suffer from noise, low contrast, and operator variability, creating challenges in clinical interpretation. To address these issues, an integrated approach combining targeted preprocessing, a lightweight CNN architecture, and balanced augmentation strategies was developed. The model demonstrates improved diagnostic consistency, effectively distinguishing normal and abnormal liver tissue patterns. This case study highlights the model’s performance, practical significance, and potential to serve as a supportive diagnostic tool in healthcare environments.
Keywords: Liver Disease Detection, Deep learning, Ultrasound imaging, Noise reduction, Medical Image Analysis
Keywords: Liver Disease Detection, Deep learning, Ultrasound imaging, Noise reduction, Medical Image Analysis
Abstract
Embedding Human Oversight in Semi-Automated Decision Structures for Critical Applications
Ajay Kumar Suwalka, Nirmal Singh*, Awanit Kumar
DOI: 10.17148/IMRJR.2026.030109
Abstract: With more and more computer-based systems used in sectors like medicine, banking, defense, and the judiciary, it becomes clear that it is very important to have continued human participation. This article explores the content, benefits and drawbacks of decision systems which maintain a clear role for human judgment particularly in high-stakes outcomes. Through examples based on concrete system implementations and by introducing a modularized model of the system, we demonstrate how blending human oversight with automated recommendations increases accountability, reduces operational errors, and gives voice to ethical responsibility. Focus Items: Iterative feedback, trust optimization and role clarity in the decision-making lifecycle. This work offers a framework to abstract the structured oversight of humans within core decision workflows associated with critical decisions and is meant to inform work in operational efficiency and governance.
Keywords: Automated decision-making, human supervision, Accountability, Ethical responsibility, Trust optimization, Iterative feedback
Keywords: Automated decision-making, human supervision, Accountability, Ethical responsibility, Trust optimization, Iterative feedback
Abstract
Artificial Intelligence in Insurance: Transforming Risk Assessment and Claims Management
K. Pandieswari*, U. Arumugam
DOI: 10.17148/IMRJR.2026.030110
Abstract: Artificial Intelligence (AI) is reshaping the insurance industry by enhancing risk assessment, streamlining claims management, preventing fraud, and improving customer satisfaction. This study explores the applications of AI in insurance operations, evaluates its effectiveness, identifies challenges in implementation, and suggests strategic approaches to maximize its benefits. Using a combination of secondary data from industry reports, academic literature, and case studies, the research highlights how AI technologies—such as machine learning, predictive analytics, natural language processing, and robotic process automation—improve operational efficiency and decision-making accuracy. Findings reveal that AI significantly enhances risk profiling, accelerates claim settlements, reduces fraudulent payouts, and strengthens policyholder trust. However, challenges such as high implementation costs, integration with legacy systems, data quality issues, and regulatory compliance must be addressed for sustainable adoption. The study concludes that strategic AI adoption, combined with human oversight and customer-centric approaches, provides insurers with a competitive advantage while delivering efficient and reliable services.
Keywords: Artificial Intelligence (AI), Insurance, Risk Assessment, Claims Management, Fraud Detection, Customer Satisfaction, Operational Efficiency, Predictive Analytics.
Keywords: Artificial Intelligence (AI), Insurance, Risk Assessment, Claims Management, Fraud Detection, Customer Satisfaction, Operational Efficiency, Predictive Analytics.
Abstract
A Multi-Metric Performance Evaluation of Indian Mutual Funds: Evidence from Arbitrage, Hybrid, and Balanced Advantage Schemes
MENDEM VARUN TEJ, DACHEPALLI PURUSHOTHAM, UNDABATLA RAMBABU, SHAIK TASNEEM TABASSUM,KONDIBOYINA PAVAN SAI
DOI: 10.17148/IMRJR.2026.030111
Abstract: This paper examines the performance of 20 mutual fund schemes from 2020 to 2025 across Arb, Agg Hybrid, and Blc Adv categories using key risk-adjusted measures such as the Sharpe Ratio, Treynor Ratio, and Jensen’s Alpha. The analysis shows that most funds failed to beat their benchmarks, with only a few displaying positive Sharpe Ratios and all reporting negative alpha. Among the categories, Blc Adv funds offered relatively better risk-adjusted results, while AFs delivered the weakest performance. Overall, the study highlights the difficulty active fund managers faced during a strong market phase and suggests that low-cost passive options and DPs may be more suitable for many investors.
Keywords: Mutual Fund Performance, Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Risk-Adjusted Returns, Benchmark Comparison, Underperformance, Active Management, Passive Investment, Portfolio Strategy, Financial Benchmarking, Investment Analysis, Fund Selection, Market Returns, Risk Management.
Keywords: Mutual Fund Performance, Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Risk-Adjusted Returns, Benchmark Comparison, Underperformance, Active Management, Passive Investment, Portfolio Strategy, Financial Benchmarking, Investment Analysis, Fund Selection, Market Returns, Risk Management.
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