Thesis Abstract

Abstract
The stock market is a complex and dynamic environment characterized by constant fluctuations and uncertainties. Predicting stock prices accurately is a challenging task that has intrigued researchers and investors for decades. With the advancements in machine learning techniques and the availability of vast amounts of financial data, there is a growing interest in leveraging machine learning algorithms to forecast stock prices. This thesis explores the applications of machine learning in predicting stock prices and aims to provide insights into the effectiveness of these techniques. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the stage for the subsequent chapters by establishing the context and rationale for the study. Chapter 2 presents a comprehensive literature review that examines existing studies and research works related to machine learning applications in predicting stock prices. This chapter synthesizes the current state of knowledge in the field, identifies gaps in the literature, and provides a theoretical framework for the research. Chapter 3 outlines the research methodology employed in this study, detailing the data collection process, selection of machine learning algorithms, feature engineering techniques, model training and evaluation methods, and performance metrics used to assess the predictive accuracy of the models. This chapter provides a transparent and systematic approach to conducting the research. Chapter 4 presents the findings of the study, including the performance evaluation results of the machine learning models in predicting stock prices. The chapter discusses the insights gained from the analysis, highlights the strengths and limitations of the models, and compares the predictive accuracy of different algorithms. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and offering recommendations for future studies. The chapter reflects on the contributions of the study to the field of financial forecasting and emphasizes the potential applications of machine learning in enhancing stock price predictions. Overall, this thesis contributes to the growing body of knowledge on the applications of machine learning in predicting stock prices. By exploring the effectiveness of machine learning algorithms in forecasting stock prices, this research offers valuable insights for investors, financial analysts, and researchers seeking to leverage advanced technologies for informed decision-making in the stock market.

Thesis Overview