Thesis Abstract

Abstract
The stock market plays a crucial role in the global economy, affecting businesses, investors, and governments alike. Predicting stock market trends is a challenging task due to the complex and dynamic nature of financial markets. In recent years, machine learning algorithms have emerged as powerful tools for analyzing vast amounts of data and making accurate predictions. This thesis focuses on the application of machine learning algorithms for predictive modeling of stock market trends with the aim of assisting investors and financial analysts in making informed decisions. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the foundation for the study by highlighting the importance of stock market prediction and the potential benefits of using machine learning algorithms in this context. Chapter Two presents a comprehensive literature review that covers ten key areas related to predictive modeling of stock market trends and the use of machine learning algorithms in finance. The review synthesizes existing research, identifies gaps in the literature, and provides a theoretical framework for the study. Chapter Three outlines the research methodology employed in this study, including data collection methods, data preprocessing, feature selection, model selection, model evaluation, and performance metrics. The chapter also discusses the ethical considerations and potential biases that may arise in the research process. Chapter Four presents the findings of the study, including the performance of various machine learning algorithms in predicting stock market trends. The chapter analyzes the results, discusses the implications of the findings, and compares the performance of different models to identify the most effective approach for stock market prediction. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications for practice, and suggesting areas for future research. The chapter also reflects on the limitations of the study and offers recommendations for improving the accuracy and reliability of predictive modeling in stock market analysis. Overall, this thesis contributes to the field of finance by demonstrating the effectiveness of machine learning algorithms in predicting stock market trends. By leveraging the power of data-driven approaches, investors and financial analysts can make more informed decisions, mitigate risks, and capitalize on opportunities in the ever-changing landscape of the stock market.

Thesis Overview