Published Papers
SUBJECT
Economics - Micro / Macro / Developmental / Behavioral
Scientific Journal
IJFMR - International Journal For Multidisciplinary Research
Name of Scholar
Ayaan Mittal
Topic
An Analysis of Oligopoly: Evidence from India’s Telecommunications Industry
About the Scholar
Ayaan is a student at Jayshree Periwal International School, Jaipur, India.
Name of Mentor
B.A., University of Essex; M.S., London School of Economics and Political Science; M.A., Virginia Tech; Ph.D., Virginia Tech
Summary
This paper examines the characteristics and operation of oligopolistic markets, with a particular focus on India's telecommunications industry. It reviews the theoretical foundations of oligopoly, including its defining characteristics and principal models, before analysing the transformation of India’s telecommunications sector following the entry of Reliance Jio in 2016. The paper evaluates the effects of Reliance Jio’s market entry on competition, market concentration, consumer welfare, and regulatory policy. It also compares India’s telecommunications market with telecommunications industries in other countries and with other oligopolistic industries to assess similarities and differences in market structure. The findings demonstrate the practical application of oligopoly theory to modern markets.
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SUBJECT
CS - AI / ML / Data Science / Quantum Computing / Blockchain / Computer Vision
Scientific Journal
IJSHRE - International Journal of Software & Hardware Research in Engineering
Name of Scholar
Kavinkrishnan Gokulakrishnan
Topic
A Conceptual Data Science Framework for Forecasting Water Availability and Guiding Crop Choice in the Cauvery Delta
About the Scholar
Kavinkrishnan is a student at New Millennium School, Bahrain.
Name of Mentor
Guided Research
Summary
Water availability in the Cauvery delta of Tamil Nadu is shaped by a chain of climatic and hydrological events that begins with Pacific Ocean sea-surface temperature anomalies and ends with the storage level of the Mettur reservoir, the principal irrigation source for over 360,000 acres of paddy. Farmers there plan three sequential cropping seasons, Kuruvai, Samba, and Thaladi, without any system that connects ENSO forecasts, upstream reservoir behaviour, and district-level groundwater vulnerability into a single, actionable signal. This paper proposes such a system as a conceptual decision-support framework, not as an implemented or field-validated forecasting model. The framework specifies how five open data streams, the NOAA Oceanic Niño Index, India Meteorological Department seasonal outlooks, Tamil Nadu reservoir telemetry, India-WRIS discharge records, and Central Ground Water Board salinity data, could be combined through an explicit weighted decision rule into a season-specific, districtspecific crop advisory, and gives the rule's equations and parameters in a form intended to be directly implementable and testable.
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SUBJECT
Economics - Micro / Macro / Developmental / Behavioral
Scientific Journal
IJFMR - International Journal for Multidisciplinary Research
Name of Scholar
Janya Gehlot
Topic
Circular Fashion: Understanding Consumer Behaviour, Knowledge and Awareness
About the Scholar
Janya is a student at Bodhi International School, Jodhpur, India.
Name of Mentor
Dr. Gabriel Katz
PhD in Economics & Statistics - California Institute of Technology, US
MSc in Economics & Statistics - California Institute of Technology, US
Summary
Circular fashion refers to an approach within the fashion industry that aims to create a closed-loop system, reducing waste and maximizing the lifespan of clothing and textiles. It aligns with the principles of the Circular Economy by rethinking how garments are designed, produced, used, and disposed of (Archana Puri,2024). This paper, by compiling and analysing prior research on the ongoing implementation of the circular economy in the fashion industry and on consumer behaviour and responses to these initiatives, presents a review of the existing literature, evaluates the success of current implementations, and identifies areas where further research and data are required for the growth of the circular fashion framework. The paper also applies relevant economic theories to analyse consumer responses to brand-led initiatives aimed at circularising production and design.
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SUBJECT
CS - AI / ML / Data Science / Quantum Computing / Blockchain / Computer Vision
Scientific Journal
IJSR - International Journal of Scientific Research
Name of Scholar
Sanjay Bharath
Topic
Are Silver Prices Predictable? A Machine-Learning Study with High Predictive Accuracy
About the Scholar
Sanjay is a student at Heartfulness International School, Chennai, India
Name of Mentor
Damianos Michaelides
PhD in Statistics - University of Southampton
BSc, (Hons) in Mathematics, Operational Research, Statistics, Economics (MORSE) - University of Southampton
Summary
This study examines the predictability of silver prices using time series and machine learning models. Daily silver price data from 2000 to 2025, comprising 6,360 observations, were analysed using ARIMA, Random Forest, and XGBoost models. The dataset was split chronologically into 70% training and 30% testing sets. Model performance was evaluated using RMSE, MAE, and MAPE. Results show that the ARIMA model achieved the lowest prediction errors, indicating strong performance in capturing short term price dynamics. Machine learning models were able to follow general trends but showed reduced accuracy during periods of high volatility. These findings suggest that traditional time series models remain effective for short term forecasting of silver prices, although incorporating additional explanatory variables may improve machine learning performance. The study highlights the challenges of forecasting volatile commodity markets and suggests directions for future research.
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SUBJECT
Business Studies - Market Research / Industry Research / International Business / FMCG / Consumer Goods
Scientific Journal
IJSRC - International Journal of Social Relevance & Concern
Name of Scholar
Siddharth Meruva
Topic
Big Data and AI in Organizational Strategy
About the Scholar
Siddharth is a student at Christ (Deemed to be) University, Bengaluru, India.
Name of Mentor
B.A., University of Essex; M.S., London School of Economics and Political Science; M.A., Virginia Tech; Ph.D., Virginia Tech
Summary
This conceptual study critically examines the transformative impact of big data and artificial intelligence (AI) on organizational strategy, consumer engagement, and digital marketing. Drawing from a broad synthesis of recent scholarly and industry literature, this paper explores significant advantages, including enhanced personalization, predictive analytics, operational efficiency, and sustainability, alongside substantial challenges such as algorithmic bias, data privacy concerns, integration complexities, and workforce readiness. Emphasis is placed on the critical role of managers and stakeholders in mitigating technological risks, fostering ethical governance, and navigating complex regulatory frameworks. The study further highlights blockchain technology’s emerging role in improving data transparency, trust, and loyalty programs, thereby reshaping customer relationships. The paper provides comprehensive recommendations for managerial practice and policy, emphasizing adaptive governance, robust data stewardship, and collaborative innovation. By integrating these insights, organizations can leverage AI and big data responsibly to capitalize on their full potential, ensuring competitive advantage while addressing ethical and operational risks in an evolving digital landscape.
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SUBJECT
Economics - Micro / Macro / Developmental / Behavioral
Scientific Journal
IJFMR - International Journal for Multidisciplinary Research
Name of Scholar
Ahana Gupta
Topic
Measuring Industry Mispricing: An Empirical Analysis of CAPM Alphas For U.S. Industry Portfolios
About the Scholar
Ahana is a student at Dhirubhai Ambani International School, Mumbai, India.
Name of Mentor
B.A., University of Essex; M.S., London School of Economics and Political Science; M.A., Virginia Tech; Ph.D., Virginia Tech
Summary
This study examines industry-level valuation through the framework of the Capital Asset Pricing Model (CAPM) using monthly data for 49 U.S. industry portfolios from January 2000 to November 2025. First, a graphical analysis of total risk and return provides preliminary evidence of a positive risk–return relationship across industries. To isolate systematic risk, CAPM regressions are estimated to obtain industry betas and Jensen’s alphas. The Security Market Line (SML) is then used to assess whether industry returns are consistent with market risk exposure. The results indicate that while beta explains a substantial portion of return variation, several industries exhibit statistically meaningful positive or negative alphas, suggesting deviations from CAPM predictions. These findings highlight cross-industry differences in risk-adjusted performance and provide insights into industry valuation and the limitations of the single-factor model.