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A Multi Criteria Decision Making Approach To Rank Alternative Investment Indices Based On Their Performance
(Journal of Informatics Education and Research, 2025) Bhatia, Amit
The selection of the ideal Alternative Investment product has been a major challenge for all investors across the world. This is because of the dynamic ever-changing financial market and the complex trade-offs between risk and return. This complexity comes from the diverse characteristics of alternative investments, where products offering high returns often come with increased volatility, while safer options provide relatively lower returns. Hence, we have adopted a Multi-Criteria Decision-Making (MCDM) model to identify the optimal investment product. In this study, we analyse the performance of eight alternative investment products (AIPs) — including S&P 500, Hedge Funds, Venture Capital, Private Equity, US Government Bonds, MSCI Emerging Markets, FTSE EPRA/NAREIT, and S&P GSCI Commodity — using three widely accepted MCDM methods: COPRAS (Complex Proportional Assessment), SAW (Simple Additive Weighting), and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The decision matrix incorporates critical performance metrics such as Standard Deviation, Mean Return, Skewness, Kurtosis, Beta, Sharpe Ratio, Sortino Ratio, and Calmar Ratio. To assign weights to these criteria we used the Analytic Hierarchy Process (AHP) to ensure a balanced evaluation. The rankings generated by these methods are often a little different, that is why we used a hybrid-ranking approach through Spearman’s Rank Correlation to consolidate the final rankings. Our findings indicate that Hedge Funds and Venture Capital emerge as the most attractive options for investors seeking high returns, while US Government Bonds and FTSE EPRA/NAREIT provide safer alternatives with lower volatility. This MCDM framework offers investors a systematic and efficient method to evaluate and rank AIPs to make informed decisions in this complex financial landscape.
Analyzing Commodity Market Volatility and Price Forecasting: A GARCH and ARIMA Model Approach
(European Economic Letters, 2025) Bhatia, Amit
Commodity trade is a cornerstone of world financial markets, providing investment opportunities, risk management, and price discovery. As commodities are inherently volatile, understanding their price fluctuations and forecasting future trendsis essential. This study examines the performance and volatility of four widely traded commoditiesin the United States -Gold, Silver, Wheat, and Crude Oil using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to measure volatility and the Autoregressive Integrated Moving Average (ARIMA) model to predict future price trends. The GARCH model effectively captures volatility clustering, a key characteristic of financial time series data, while ARIMA analyzes historical patterns for price prediction. Using a decade's worth of dailyhistorical price data from secondary sources, this research provides a robust dataset for in-depth analysis. Additionally, thisstudy highlightsthe need for advanced predictive models that enhance accuracy during market fluctuations. By analyzingGARCH and ARIMA applications in commodity trading, this research contributes to financial modeling and risk management literature, encouraging further exploration of alternative forecasting methods.
Data-Driven Decision Making for Perishable Food Supply Chains: Insights from Demand Forecasting Models
(Advances in Consumer Research, 2025) Bhatia, Amit
In recent years, food supply chain has gained considerable attention as compared to other supply chain systems. This can be attributed to the fact that fresh food products are perishable items which inherently has very short shelf life. Further, manual estimation of demand of these products often leads to demand underestimation and overestimation, which adversely affects revenues of the retailer. Therefore, effective demand forecasting can help to reduce food wastage as well as financial losses. The primary objective of this research is to investigate advanced machine learning models such as Random Forest Regressor, XGBoost, and Polynomial Regression, for improving demand forecasting accuracy. In this work, we have specifically considered highly perishable items such as ladyfinger and tomato. Performance of the proposed models are evaluated on six years of market data from Maharashtra, India, using the Mean Absolute Percentage Error (MAPE) metric. Findings indicate that the Random Forest Regressor achieves the highest accuracy, reducing forecasting errors and enabling better decision-making in inventory and resource management. The proposed approach provides valuable insights for stakeholders, including farmers, distributors, and retailers, to minimize waste, optimize inventory, and ensure sustainable supply chain practices.
Evaluating Effectiveness of IPO Pricing in the Indian Markets
(Accountancy Business and the Public Interest, 2025) Bhatia, Amit; Mohammed, Shoaib; Homavazir, Malcolm
The Indian IPO market has also been growing at a rapid rate in the recent past with companies from various sectors of business listing on the market to mobilize funds. Indian IPO valuations are influenced by various factors such as economic environments, regulatory announcements, sentiment of investors, and firm-specific events. This study aims to analyze the trend in IPO pricing in India from 2016 to 2025 in terms of average IPO prices, IPO issuance, most active IPO sectors, and performance of IPOs post-listing. The study uses data from various sources such as company prospectuses, stock exchange websites, and financial newspapers. The findings show that the average IPO valuations have increased significantly over the years, with the highest valuation achieved by the technology, financial service, and consumer goods sectors. The study further picks on the impact of retail participation on IPO valuations, and investor sentiment as determinants for the success of IPOs. Overall, the study illuminates the dynamics of the IPO market in India and provides valuable information to investors, policymakers, and market participants.
LLM-Augmented Natural Language Query Generation for NoSQL Inventory Management
(International Journal of Environmental Sciences, 2025) Bhatia, Amit
Successful inventory management mainly depends on effective querying and analyzing large numbers of database records. Conventional database queries necessitating structured query language (SQL) or NoSQL syntax impose an insurmountable barrier to nontechnical users. This paper proposes a state-of-the-art system based on a Large Language Model (LLM) that converts natural language queries into high-efficiency optimized MongoDB queries. The system uses transfer learning approaches to optimize transformer-based models, thus leveraging Hugging Face's Sentence-Transformer’s library. The models are optimized to identify optimal embeddings for query understanding. Furthermore, a selection of vector databases was tested for performance in semantic retrieval, where FAISS demonstrated high performance in retrieval speed for embeddings of high dimensionality. The proposed system integrates few-shot prompting techniques to improve contextual query understanding, utilizing LLaMA 3.3-70B to create robust and adaptive queries. By subjecting the performance of various embedding techniques and retrieval approaches to systematic testing, this paper provides a framework for dynamic query-to-database translation optimization, minimizing execution latency while maximizing retrieval accuracy. The results affirm that integrating state-of-the-art LLMs and optimized vector retrieval greatly enhances query performance, simplifying real-time inventory management for nontechnical users.
The Opportunity Cost of Early Saving: A Study on Delayed Financial Planning in India
(International Journal of Environmental Sciences, 2025) Bhatia, Amit
This research paper examines the opportunity cost of early saving by evaluating the financial outcomes of individuals who begin investing in their early 20s versus those who delay investing until their late 20s or early 30s. The study aims to quantify how compounding, market conditions, and inflation trends impact wealth accumulation over time. By conducting a comparative analysis across two asset classes—equities (NIFTY 50 ETF) and Gold ETFs, the research assesses the significance of early investing in enhancing investment efficiency, risk-adjusted returns, and long-term financial stability. The study employs statistical hypothesis testing (Independent Samples t-Test) to analyze differences in Savings Efficiency Ratio (SER), Sharpe Ratio, and Compounded Annual Growth Rate (CAGR) between early and late savers. The findings reveal that while late investors appear to achieve higher nominal returns in recent years, this advantage is largely driven by market timing biases and inflationary distortions, which diminish real purchasing power. The study further highlights that early saving consistently results in superior investment efficiency, mitigating market volatility and compounding wealth more effectively over time. The research contributes to the broader discussion on financial planning and wealth management by reinforcing the importance of early investing as a long-term wealth-building strategy. The insights from this study emphasize the need for young individuals to adopt structured savings and disciplined investing habits at an early stage, ensuring greater financial security, inflation protection, and sustainable wealth accumulation over their lifetime.

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