Powering Micro-Businesses: A Theoretical Analysis of Electricity Reliability, Productivity and Capital Investment in Maharashtra
This paper examines how electricity reliability may affect the operating hours and capital investment decisions of micro-businesses in Maharashtra, India. While access to electricity has expanded substantially, having a grid connection does not necessarily guarantee a reliable or predictable supply. For micro-businesses operating with limited financial resources, frequent power interruptions and poor power quality can reduce productive operating time, leave electricity-dependent equipment underutilised, increase operating costs and create uncertainty around future investment. The study adopts a theoretical secondary research approach, drawing on existing literature on electricity reliability, firm productivity, capacity utilisation, infrastructure and investment under uncertainty. Rather than estimating a direct causal relationship using original business-level data, the paper synthesises these strands of literature to develop a conceptual framework for understanding how electricity reliability can influence micro-business decisions. The analysis focuses on two main channels: the effect of electricity reliability on productive operating time and capacity utilisation, and its potential effect on the expected returns and risks associated with capital investment. The paper argues that the economic value of electricity cannot be fully captured by measuring access alone. For micro-businesses, the reliability and predictability of supply may influence how effectively existing productive resources are used and the conditions under which further investment becomes worthwhile. The study concludes by considering the implications for electricity and MSME policy in Maharashtra and identifying areas for future empirical research.
Published by: Kavya Mehta
Author: Kavya Mehta
Paper ID: V12I5-1176
Paper Status: published
Published: October 3, 2026
A Study to Assess the Effectiveness of Structured Teaching Program on Knowledge Regarding Osteoporosis Among Post-Menopausal Women of Selected Areas of Distt. Mandi (H.P)
Abstract: Osteoporosis is a silent, progressive disease characterized by decreased bone mass and structural deterioration, leading to an increased risk of fractures. Postmenopausal women are particularly vulnerable due to hormonal changes, especially the decline in estrogen levels. In India, the prevalence of osteoporosis is high among post-menopausal women, particularly in rural areas where awareness and access to preventive healthcare are limited. Educational interventions like structured teaching programs can help bridge the knowledge gap and promote preventive practices. Aim :To assess the effectiveness of a structured teaching program on knowledge regarding osteoporosis among post-menopausal women in selected areas of District Mandi, Himachal Pradesh. Materials and Methods: A pre-experimental, one-group pretest/post-test designwas adopted. A total of 100 post-menopausal women were selected through non- probability purposive sampling. A structured questionnaire was used to assess their knowledge regarding osteoporosis before and after the intervention. The structured teaching program included lectures, visual aids, and demonstrations related to osteoporosis prevention. Data were analysed using descriptive and inferential statistics; the effectiveness of the intervention was tested using the paired t-test, and associations with demographic variables were analysed using the chi-square test. Findings: Thepretest findings revealed that 5% had good knowledge ,29% ofwomen had poor knowledge, 66% had average knowledge. After the intervention, 62% demonstrated good knowledge, 38% had average knowledge, and none had poor knowledge. The mean knowledge score increased from 13.56 (SD = 4.76) in the pretest to 22.20 (SD = 3.43) in the post-test.The paired t-test showed a statistically significant improvement (t = 14.28, p = 0.00001*). A significant association was found between knowledge levels and variables such as occupation and type of family. Conclusion: The structured teaching program was effective in significantly enhancing the knowledge of post-menopausal women regarding osteoporosis. Educational interventions can play a critical role in promoting awareness and preventive practices, especially in rural settings where health literacy is low.
Published by: Mrs. Shivani, Mrs. Priyanka sharma, Mrs. Sunita devi
Author: Mrs. Shivani
Paper ID: V12I5-1172
Paper Status: published
Published: September 30, 2026
Stocks, Bonds, Gold, and Bitcoin: A Comparative Analysis of Risk-Adjusted Returns from 2022 to 2024
This paper examines the relationship between U.S equities, gold, Bitcoin and short term government debt over the three years from January 2022 to December 2024, a period defined by the fastest Federal Reserve rate hiking cycle in four decades, the beginnings of a cutting cycle, alongside the January 2024 approval of spot Bitcoin exchange-traded funds. Using a monthly return rate data and a risk-free rate derived from Treasury bill yields, this study calculated Sharpe and Sortino ratios, a six-month rolling beta for Bitcoin and gold against the S&P 500 as well as a pairwise correlation matrix across all three price based assets which is set against the Federal Funds Effective Rate as a contextual overlay. The findings show that gold recorded the highest risk-adjusted return among the three assets. This was the case for both the Sharpe and Sortino ratios. Bitcoin’s beta against the S&P 500 also changed significantly during the period studied. It moved from below zero to above four within a few months. This increase occurred during the period following the approval of the Bitcoin ETFs. However, Bitcoin’s beta later fell again as the Federal Reserve approached its first rate cut in late 2024. Bitcoin’s correlation with the S&P 500 (0.571) was substantially stronger than gold’s (0.243). In comparison, Bitcoin and gold themselves had only a weak correlation of 0.299. Overall, these results suggest that Bitcoin behaved more like a volatile and equity-adjacent asset rather than an independent store of value during this period. The study connects this pattern to prospect theory and loss-averse behaviour.
Published by: Hrehaan Anand
Author: Hrehaan Anand
Paper ID: V12I5-1170
Paper Status: published
Published: September 25, 2026
Can Derivative-Based Hedging Improve the Risk-Adjusted Returns of Portfolios Holding Alternative Investments?
The inclusion of alternative investments in modern portfolio allocation can significantly increase diversification because investors want returns that are not fully explained by traditional equity and fixed-income holdings. Alternative investments are here defined as financial assets outside stocks, bonds and cash (e.g., hedge funds, private equity, private credit, real estate, commodities and infrastructure). Although these assets improve diversification and have attractive return potential they also come with risks which are often harder to observe or manage than those of traditional securities such as market risk, liquidity risk, credit risk, leverage risk, currency risk and interest rate rise plus non-linear/asymmetric return distributions. Derivatives like futures, options, forwards and swaps allow investors to hedge specific components of this risk without liquidating the underlying positions. This review synthesizes existing literature on derivative-based hedging to test whether it improves risk-adjusted returns of portfolios containing alternative investments. The literature suggests that hedging effectiveness depends on the relationship between the hedging instrument and the exposure being hedged, hedge ratio achieved, investment horizon and market conditions. Hedging costs are incurred, volatility reduction does not translate into improved risk-adjusted return because hedging also requires upside participation so volatility reduction doesn’t lead to higher risk adjusted return. From hedge funds, commodities and equity-linked strategies evidence shows that hedging is generally more reliable where underlying risk is systematic and hedging instrument tracks exposure closely but less reliable where risk is idiosyncratic, illiquid or hard to observe which is common in private equity and private credit. In general the literature seems to indicate that derivative-based hedging can enhance risk-adjusted returns of alternative-investment portfolios only if it is applied to identifiable systematic and hedgeable risks, costs are taken into account and so on. Hedging should therefore be viewed as a tool for managing risk exposure and improving efficiency in portfolio management rather than as a guaranteed means of increasing dollar returns.
Published by: Himanshu Garg
Author: Himanshu Garg
Paper ID: V12I5-1151
Paper Status: published
Published: September 23, 2026
Financial Fundamentals and Stock Market Performance: Evidence from BSE Top 50 Companies
This study examines the relationship between market performance and financial performance of companies listed in the BSE Top 50 index of the Bombay Stock Exchange. The primary objective is to analyze whether strong financial fundamentals translate into superior market returns and to evaluate the risk–return trade-off among leading Indian corporations over medium- and long-term periods. Approach: The study adopts a quantitative and empirical research design using secondary data collected from annual reports, financial statements, and stock market databases. A purposive sample of 50 top-performing companies from the BSE Top 50 was selected. Financial performance was measured using key indicators such as Return on Equity (ROE), Earnings Per Share (EPS), Net Profit Margin, and Return on Assets (ROA), while market performance was evaluated through stock returns, volatility, beta, and risk-adjusted measures including the Sharpe Ratio, Treynor Ratio, and Jensen's Alpha. Data were analyzed over 3-year, 5-year, and 10-year periods using descriptive statistics, correlation analysis, and regression techniques to determine the strength and significance of relationships. Originality: Unlike prior studies that examine either financial performance or stock market performance independently, this research integrates both dimensions to assess whether financial strength consistently leads to superior market valuation and investor returns within India's blue-chip segment. Practical Implementation: The findings provide valuable insights for investors, portfolio managers, and financial analysts by highlighting whether investment decisions should rely primarily on financial fundamentals, market trends, or a combination of both. The study supports evidence-based investment strategies for long-term wealth creation in the Indian equity market.
Published by: Chetna Tansukhbhai Parmar
Author: Chetna Tansukhbhai Parmar
Paper ID: V12I5-1167
Paper Status: published
Published: September 23, 2026
A Hybrid Deep Learning Framework Integrating Variational Autoencoder with Self-Attention and MobileNet for Enhanced Malware Detection: An Optimized XGBoost Approach Using EMBER 2018 and 2024 Datasets
The increase in advanced types of malwares is one of the factors that are very challenging to traditional detection systems. In this paper, a new hybridized deep learning model is proposed, which combines Variational Autoencoder (VAE) and self-attention architecture and MobileNet with strong malware detection and classification. It is proposed that the research methodology will use the EMBER 2018 and EMBER 2024 datasets, with extensive preprocessing and normalization processes to improve the ability of features to be represented. Our dual-branch model learns complementary information the VAE-attention branch learns contextual dependencies of latent representations, whereas the MobileNet branch learns hierarchical visual patterns of malware representation in the form of byte representations. These attributes are combined with the help of interconnecting layers and trained with XGBoost to provide a final classification. As reported by experiment, binary classification and multi-class classification accuracy of 99.47 and 98.23 respectively are significantly better than the current state-of-the-art methods on EMBER 2024. The framework has a 3.2% improvement of detection rate with 42% false positives compared to traditional methods.
Published by: Vinita, Manju Mandot
Author: Vinita
Paper ID: V12I5-1164
Paper Status: published
Published: September 23, 2026
