Published Papers

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.

This paper explores the impact of constitutional frameworks on national governance by analyzing three distinct types of constitutions: completely written, completely unwritten, and those that are partially written and partially unwritten. This study investigates how each type of constitution influences the functionality and adaptability of government institutions, focusing on their roles in fostering or hindering national progress. Through comparative analysis, the paper highlights that while the form of the constitution plays a role in shaping governmental operations, the key determinants of a country's trajectory—whether towards progress or regression—are the effectiveness, flexibility, and responsiveness of its institutions. By examining examples of various countries, the research underscores that institutional dynamics, leadership quality, and societal engagement are crucial factors in determining a nation's success, regardless of its constitutional format.

This paper investigates the impact of corruption on economic performance, focusing on the “grease the wheels” and “sand the wheels” hypotheses. The “grease the wheels” hypothesis suggests that corruption might enhance efficiency by bypassing red tape, while the “sand the wheels” hypothesis argues that corruption increases transaction costs and disrupts economic growth. Analysing data from studies conducted on both advanced and developing economies, we find corruption “sands the wheels” of advanced economies while it “greases the wheels” of developing economies. However, by analysing growth modes such as the Solow-Swan model as well as the Endogenous growth model to understand the effect of corruption on a larger scale, it was found that corruption on a higher level in a country’s government is more likely to hinder economic growth. The findings underscore the need for context-specific anti-corruption strategies.

The January effect is one of the most documented calendar anomalies that exist in the stock market, and this anomaly directly challenges the Efficient Market Hypothesis (EMH) by stating that there exists a pattern in the stock market where a return can be gained. One popular explanation for the January Effect is the tax-loss selling hypothesis. This hypothesis states that investors sell off losing stocks in their portfolio at the end of the year to realize capital losses for tax purposes and subsequently repurchase them in January, which drives up the stock prices. Evidence from markets such as the United States, United Kingdom, Hong Kong, Singapore, and Thailand is collected and analyzed to show the effects of different tax structures and investor behaviors on the January Effect. The findings indicate that the taxloss selling hypothesis substantially accounts for the January Effect found in the markets that levy capital gains tax. Other factors, such as investor sentiment and institutional behaviors also account for the existence of the anomaly for markets free of capital gains tax.