Thesis Abstract

Abstract
In recent years, the integration of machine learning techniques in finance has gained significant attention due to its potential to enhance the prediction of stock market trends. This thesis explores the applications of machine learning algorithms in predicting stock market trends and aims to contribute to the existing body of knowledge in this field. The research focuses on developing predictive models using historical stock market data and various machine learning algorithms to forecast future trends accurately. The thesis begins with an introduction that highlights the background of the study, problem statement, objectives, limitations, scope, significance, structure, and definition of key terms. The literature review in Chapter Two examines existing studies on machine learning applications in stock market prediction, providing a comprehensive overview of the current state of research in this area. Chapter Three details the research methodology, including data collection, preprocessing techniques, feature selection, model development, and evaluation strategies. The methodology section outlines the steps taken to build and optimize machine learning models for predicting stock market trends effectively. Chapter Four presents a detailed discussion of the findings obtained from applying various machine learning algorithms to historical stock market data. The chapter evaluates the performance of each model, identifies key factors influencing prediction accuracy, and discusses the implications of the results for stock market forecasting. Finally, Chapter Five offers a conclusion and summary of the thesis, highlighting the key findings, contributions to the field, limitations of the study, and suggestions for future research. The conclusion emphasizes the significance of machine learning techniques in improving stock market trend prediction and underscores the potential for further advancements in this area. Overall, this thesis provides valuable insights into the applications of machine learning in predicting stock market trends, offering a foundation for future research and practical implications for investors, financial analysts, and policymakers. The findings of this study contribute to the growing body of knowledge on the intersection of machine learning and finance, opening new avenues for enhancing stock market prediction accuracy and decision-making processes.

Thesis Overview