Thesis Abstract

Abstract
The financial markets have always been a subject of interest due to their dynamic nature and the potential for significant gains or losses. Predicting stock market trends accurately has long been a goal for investors, analysts, and researchers alike. Traditional methods of stock market analysis often fall short in capturing the complex patterns and behaviors exhibited by financial markets. In recent years, the application of machine learning techniques has shown promise in improving the accuracy and efficiency of stock market predictions. This thesis explores the application of machine learning algorithms in predicting stock market trends. The primary objective is to develop a predictive model that can effectively forecast stock price movements based on historical data and other relevant factors. The study aims to investigate the performance of various machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, in predicting stock market trends. Chapter one provides an introduction to the research topic, offering background information on the significance of predicting stock market trends and highlighting the limitations and scope of the study. The chapter also outlines the research problem, objectives, and the structure of the thesis. Chapter two presents a comprehensive literature review on the application of machine learning in stock market prediction. The review covers various studies and methodologies that have been employed in this field, highlighting the strengths and weaknesses of different approaches. Chapter three details the research methodology employed in this study, including data collection, preprocessing, feature selection, model training, and evaluation. The chapter also discusses the selection of performance metrics and validation techniques used to assess the accuracy and reliability of the predictive models. Chapter four presents the findings of the study, including the performance evaluation of different machine learning algorithms in predicting stock market trends. The chapter analyzes the results, discusses the implications of the findings, and compares the performance of the models against each other. Chapter five concludes the thesis by summarizing the key findings, discussing the practical implications of the research, and offering recommendations for future research in this area. The study contributes to the existing body of knowledge by demonstrating the potential of machine learning techniques in improving the accuracy of stock market predictions. In conclusion, this thesis provides valuable insights into the application of machine learning in predicting stock market trends. The findings of the study have implications for investors, financial analysts, and researchers seeking to enhance their understanding of stock market dynamics and improve their forecasting capabilities.

Thesis Overview