Thesis Abstract

Abstract
This thesis explores the applications of machine learning techniques in predicting stock market trends. The stock market is a complex and dynamic system influenced by various factors, making accurate predictions challenging. Machine learning algorithms offer a promising approach to analyze and interpret market data to forecast price movements. The study aims to investigate the effectiveness of machine learning models in predicting stock market trends and to provide insights into the key factors that influence stock prices. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The definitions of key terms related to the study are also presented in this chapter to provide a clear understanding of the research context. Chapter 2 presents a comprehensive literature review on existing studies related to the applications of machine learning in stock market prediction. The review covers various machine learning techniques, datasets used, performance metrics, and key findings from previous research. This chapter aims to provide a theoretical foundation for the research and identify gaps in the existing literature. Chapter 3 details the research methodology employed in this study. It includes the research design, data collection methods, feature selection techniques, model selection, evaluation metrics, and validation strategies. The chapter also discusses the preprocessing steps and model training processes to ensure the robustness and reliability of the predictive models. Chapter 4 presents an in-depth analysis of the findings from applying machine learning models to predict stock market trends. The chapter discusses the performance of different algorithms, feature importance, model interpretability, and the impact of various factors on prediction accuracy. The findings are interpreted to provide insights into the underlying patterns and trends in the stock market data. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting future directions for further studies. The chapter highlights the contributions of the research to the field of stock market prediction using machine learning techniques and emphasizes the practical applications of the findings in real-world investment decision-making. Overall, this thesis contributes to the growing body of knowledge on the applications of machine learning in predicting stock market trends. By leveraging advanced algorithms and techniques, this research aims to enhance the accuracy and efficiency of stock market predictions, providing valuable insights for investors, financial analysts, and researchers in understanding and navigating the complexities of the stock market.

Thesis Overview