Published Papers

This paper investigates the role of psychological biases in financial bubbles and their bursts, challenging the traditional view of rational investor behavior proposed by the Efficient Market Hypothesis. By examining historical bubbles like the South Sea and Dot-Com bubbles, and analyzing biases such as overconfidence, herding, and confirmation bias, the paper aims to reveal how these cognitive errors influence investor actions during crises. Additionally, this paper compares past bubbles with current cryptocurrency market trends. Ultimately, the aim of this paper is to enhance our understanding of investor psychology and its impact on financial stability in the economy

This research paper investigates the impact of U.S. monetary policy changes, specifically interest rate adjustments by the Federal Reserve, on asset prices in the stock and real estate markets. By examining both expansionary and contractionary policies, the study aims to understand the immediate and longterm effects on these asset classes. The findings emphasize the significant influence of monetary policy on the economy, with effects varying based on economic conditions and regional factors. However, the study's reliance on historical data, focus on U.S. policy, and assumption of constant variables present limitations in capturing the complexities of current and future economic conditions.

This research paper investigates the effect of interest rate changes on stock prices, focusing on the contrasting impacts in developing and developed countries. Utilizing regression analysis, the study examines data from five developing countries and five developed countries. The results reveal a significant inverse relationship between interest rates and stock prices. These findings underscore the heightened sensitivity of developing markets to monetary policy adjustments, attributed to factors such as lower market liquidity, higher dependence on foreign capital and weaker financial infrastructure. Conversely, developed markets demonstrate greater resilience and stability due to more robust financial systems and higher investor sophistication. This study contributes to the existing literature by highlighting the necessity of context-specific policy approaches and provides crucial insights for policymakers and investors navigating the complex interplay between interest rates and stock market dynamics in diverse economic environments.

This research paper aims to test and compare natural remedies to antifungal medications to see if they can be viable replacements for the medications. It aims to offer alternatives to people who may not have access to antifungal medications and avoid the overuse of medication. Through incubation, it found that garlic was an effective antifungal and the extent of its effect was the same as the antifungal medications, while castor oil and coconut oil were not as effective. Therefore, it states that garlic is a viable alternative to antifungal medications for yeast infections and encourages further testing to find more replacements for various types of infections.

Energy can neither be created nor can it be destroyed. It can only change from one form to another. This paper examines if the law of conservation of energy holds true in projectile motion. With advances in computing, we do not require artillery guns and sophisticated equipment for testing these concepts now. A Python-based computer simulation coded with the appropriate rules and set to accept the correct parameters (Mass of the object, Angle of Projection, and Initial Velocity) can conduct an infinite number of experiments and simplify reporting. Through this paper, we test the law of conservation of energy at different points along the projectile’s trajectory and for three different parameters namely Mass of the object, Angle of Projection, and Initial Velocity and present our findings.

An artificial neural network is a model from the field of machine learning that was inspired by the structure and function of the brain. Whilst deep learning involves neural networks with multiple layers between the input and output layers. This paper shows how neural networks work, and describes various features. Backpropagation is explained in detail, and the common issues that one can face whilst training a neural network are described. Methods of mitigating these issues are also given. Finally, a neural network is employed to build a daily Bitcoin trading system. The results were inferior to multiple linear regression. The neural network likely suffered from overfitting, but regularisation using dropout helped to mitigate this.