Thesis Abstract

Abstract
This thesis explores the application 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 have shown promise in analyzing large datasets and identifying patterns that can help forecast stock price movements. The research aims to investigate the effectiveness of machine learning models in predicting stock market trends and to provide insights into the potential benefits and limitations of these approaches. Chapter 1 provides an introduction to the research topic, including the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also defines key terms relevant to the study. Chapter 2 presents a comprehensive literature review that covers ten key areas related to machine learning, stock market prediction, and previous studies in the field. This section provides a critical analysis of existing research, identifies gaps in the literature, and sets the foundation for the current study. Chapter 3 details the research methodology employed in this study, including data collection methods, feature selection techniques, model development, evaluation metrics, and validation procedures. The chapter outlines the steps taken to implement machine learning algorithms for stock market trend prediction. Chapter 4 presents a detailed discussion of the findings obtained from applying machine learning models to predict stock market trends. The chapter analyzes the performance of different algorithms, evaluates the accuracy of predictions, and discusses the implications of the results in the context of stock market forecasting. Chapter 5 serves as the conclusion and summary of the thesis, highlighting the key findings, implications for practice, and recommendations for future research. The chapter provides a synthesis of the research outcomes and reflects on the contributions of the study to the field of stock market prediction using machine learning techniques. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning in predicting stock market trends. By exploring the potential of advanced algorithms to forecast stock price movements, this research offers valuable insights for investors, financial analysts, and researchers interested in leveraging data-driven approaches for better decision-making in the stock market.

Thesis Overview