Thesis Abstract

Abstract
The rapid advancement of technology and the availability of vast amounts of financial data have led to an increased interest in applying machine learning techniques to predict stock prices. This thesis investigates the application of machine learning algorithms in predicting stock prices, with a focus on enhancing the accuracy and efficiency of stock price forecasting models. The study examines the potential of machine learning models, such as neural networks, support vector machines, and random forests, in predicting stock prices based on historical data and various financial indicators. The research begins with a comprehensive introduction that outlines the background of the study, defines the problem statement, sets the objectives of the study, discusses the limitations and scope of the research, highlights the significance of the study, and provides an overview of the thesis structure. The introduction lays the foundation for understanding the importance of utilizing machine learning techniques in predicting stock prices and the potential implications for investors and financial markets. Chapter two presents a detailed literature review that explores existing research on stock price prediction using machine learning approaches. The review covers key concepts, methodologies, and findings from previous studies, providing a comprehensive understanding of the current landscape in the field of stock price forecasting. The review also identifies gaps in the literature and highlights areas for further research. Chapter three focuses on the research methodology employed in this study. The chapter discusses the data collection process, feature selection techniques, model training and evaluation methods, and the criteria used to assess the performance of machine learning models in predicting stock prices. The research methodology section provides a clear framework for conducting the empirical analysis and testing the effectiveness of different machine learning algorithms in stock price prediction. Chapter four presents a thorough discussion of the findings obtained from the empirical analysis. The chapter evaluates the performance of various machine learning models in predicting stock prices and compares their accuracy, robustness, and efficiency. The discussion highlights the strengths and limitations of each model, identifies factors that influence stock price prediction accuracy, and offers insights into improving the effectiveness of machine learning algorithms in financial forecasting. Finally, chapter five concludes the thesis by summarizing the key findings, discussing the implications of the research results, and providing recommendations for future research directions. The conclusion underscores the significance of utilizing machine learning techniques in predicting stock prices and emphasizes the potential value of incorporating advanced data analytics in investment decision-making processes. In conclusion, this thesis contributes to the existing body of knowledge on stock price prediction by demonstrating the effectiveness of machine learning algorithms in enhancing forecasting accuracy. The study offers valuable insights for investors, financial analysts, and researchers seeking to leverage machine learning techniques for more accurate and timely stock price predictions.

Thesis Overview