Thesis Abstract

Abstract
The financial market is a complex and dynamic environment where investors seek to make informed decisions to maximize their returns. Prediction of stock prices has always been a challenging task due to the multitude of factors that influence the market. In recent years, machine learning algorithms have gained popularity for their ability to analyze large datasets and identify patterns that can be used to predict stock prices. This thesis explores the application of machine learning techniques in predicting stock prices and evaluates the effectiveness of these models in the financial market. Chapter 1 provides an introduction to the study, including the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the foundation for the research and outlines the key areas that will be explored in the thesis. Chapter 2 presents a comprehensive literature review of existing studies and research findings related to the application of machine learning in predicting stock prices. The review examines various machine learning algorithms, methodologies, and approaches used in previous studies and highlights the strengths and limitations of these models. Chapter 3 focuses on the research methodology used in this study. The chapter outlines the research design, data collection methods, variables, sampling techniques, and the machine learning algorithms that will be employed to predict stock prices. The methodology section provides a clear framework for conducting the research and analyzing the data. Chapter 4 presents a detailed discussion of the findings obtained from applying machine learning algorithms to predict stock prices. The chapter analyzes the performance of the models, evaluates their accuracy and efficiency, and discusses the key factors influencing the predictions. The findings provide valuable insights into the effectiveness of machine learning in predicting stock prices. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and offering recommendations for future studies. The chapter highlights the significance of the study in the context of the financial market and emphasizes the potential applications of machine learning in improving stock price predictions. Overall, this thesis contributes to the growing body of research on the application of machine learning in predicting stock prices. By leveraging advanced algorithms and techniques, investors can gain valuable insights into market trends and make more informed decisions. The findings of this study have the potential to enhance the efficiency and accuracy of stock price predictions, ultimately benefiting investors and stakeholders in the financial market.

Thesis Overview