Thesis Abstract

Abstract
The stock market is a complex and dynamic system that is influenced by numerous factors, making it challenging for investors to predict future trends accurately. In recent years, the application of machine learning techniques has gained traction in the financial sector as a promising tool for analyzing vast amounts of data and making informed investment decisions. This thesis explores the use of machine learning algorithms to predict stock market trends and improve investment strategies. Chapter One provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The chapter also includes definitions of key terms used throughout the study to provide clarity and context. Chapter Two presents a comprehensive literature review, examining existing research on machine learning applications in stock market prediction. The review covers various machine learning algorithms, data sources, feature selection techniques, and evaluation metrics used in predicting stock market trends. Chapter Three outlines the research methodology employed in this study, detailing the data collection process, data preprocessing techniques, feature engineering methods, and the selection and implementation of machine learning algorithms. The chapter also discusses the evaluation criteria and performance metrics used to assess the predictive accuracy of the models. Chapter Four presents the findings of the research, including the performance evaluation of different machine learning models in predicting stock market trends. The chapter discusses the strengths and limitations of each model and provides insights into the factors influencing their predictive accuracy. Chapter Five 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 machine learning in predicting stock market trends and its potential impact on investment decision-making. In conclusion, this thesis contributes to the growing body of research on the application of machine learning in the financial sector, particularly in predicting stock market trends. By leveraging advanced machine learning algorithms and techniques, investors can gain valuable insights into market dynamics and make more informed investment decisions, ultimately enhancing their portfolio performance and risk management strategies.

Thesis Overview