Published Papers

The purpose of this paper is to examine how social media and influencers are impacting food safety and standards in India. I have looked at a category of food supplements earlier marketed as ‘health beverages’, and how the issue of sugar content in these was flagged off via social media, eventually leading to a change in corporate behavior

This paper offers a comprehensive analysis of bank runs, tracing their evolution from the Great Depression to the COVID-19 pandemic. It explores the fundamental concept of bank runs, their shifting causes and consequences over the past century, and the role of public psychology in exacerbating these events. The paper highlights how the general public’s reactions can significantly influence the course of a bank run, noting how panic triggered by news of a bank run can rapidly escalate, causing individuals to rush to withdraw their funds and potentially creating widespread chaos and distress. Furthermore, the paper examines how policy adjustments and new implementations address public psychology and attempt to mitigate the risk of bank runs. It includes detailed assessments of the evolution of the FDIC and capital controls in response to various financial crises. Overall, the paper provides a focused analysis of the historical context and policy responses, evaluating them in relation to different financial crises over time.

This paper assesses whether climate change has an effect on mangrove forests using the Google Earth Engine (GEE) computing platform. Applying the 35+ year quasi-global rainfall CHIRPS dataset with the GEE platform provided the Precipitation and Evapotranspiration data. This data can be used to correlate with the climate variables, CO2 emissions and Temperature. 

This paper aims to analyse the correlation between Precipitation and Evapotranspiration with the climate variables to assess the climate change effect on the Al Zorah Natural Reserves mangrove in the UAE.

This research explores the application of neural networks to predict gold prices, leveraging machine learning techniques to enhance financial analysis based on historical data. The research involves the development of a predictive model utilizing data normalization, the Adam optimiser, and the mean square error function. The model was trained on historical prices in the last 20 years, achieving an impressively high accuracy of 96.79% in predicting the future price one year ahead. Despite the promising results, the research identified key challenges, such as the model’s sensitivity to input data and the inherent complexity of financial markets. These findings emphasize the potential of machine learning in finance, while also highlighting the need for ongoing refinement and the incorporation of broader, more comprehensive datasets to improve the model’s precision. This study essentially focuses on the interlacing of machine learning and the world of financial analysis, offering key insights for future research and practical applications in market prediction.

Thirty distinct regression algorithms were employed to build Bitcoin trading systems. Twenty-five of them generated a profit before costs. The best methods were ensemble methods that create multiple decision trees on random subsets of either features or data. Only three of the algorithms outperformed linear regression. This could be because there were no significant nonlinearities present (which would be surprising in a financial market), or, more likely, most of the algorithms were overfitting the training data because it was relatively sparse (daily), and the dynamics of an immature market changed through time.

The paper explores the relationship between foreign direct investment (FDI) and income inequality, using India and Japan as case studies. Despite similar levels of FDI, these countries exhibit different levels of inequality. The paper argues that factors beyond economic development, such as education systems, FDI distribution, and government policies, play crucial roles in shaping this relationship. The paper demonstrates that the relationship between FDI and income inequality is complex and influenced by factors beyond economic development. Understanding these factors is essential for policymakers seeking to harness the benefits of FDI while mitigating its potential negative impacts on inequality.