Thesis Abstract

Abstract
The world of finance has always been dynamic and unpredictable, with stock prices fluctuating based on various factors and events. Traditional methods of stock price prediction have often fallen short in providing accurate and reliable forecasts. However, with the advancements in technology and the rise of machine learning algorithms, there is a growing interest in utilizing these tools to enhance stock price prediction models. This study aims to explore the application of machine learning in predicting stock prices and evaluate its effectiveness in comparison to traditional methods. The research begins with a comprehensive introduction, providing a background of the study and highlighting the significance of utilizing machine learning in stock price prediction. The problem statement identifies the limitations of existing prediction models and sets the stage for the objectives of the study, which include developing and testing machine learning algorithms for stock price prediction. The scope and limitations of the study are outlined to provide a clear understanding of the research boundaries. Chapter two delves into a detailed literature review, covering ten key aspects related to stock price prediction, traditional methods, and the application of machine learning algorithms in financial forecasting. This section aims to build a strong theoretical foundation and understand the current landscape of stock price prediction research. Chapter three focuses on the research methodology, outlining the steps involved in data collection, preprocessing, feature selection, model training, and evaluation. The methodology section also discusses the selection of machine learning algorithms such as neural networks, support vector machines, and random forests for stock price prediction, along with the evaluation metrics used to assess the model performance. Chapter four presents the findings of the study, including the performance evaluation of the machine learning models in predicting stock prices. The discussion covers the accuracy, precision, recall, and other relevant metrics to compare the effectiveness of machine learning algorithms against traditional methods. The results obtained from the experiments are analyzed and interpreted to draw meaningful conclusions. Lastly, chapter five summarizes the key findings of the study and presents the conclusions drawn from the research. The implications of using machine learning in stock price prediction are discussed, along with recommendations for future research in this area. The thesis concludes with a reflection on the significance of this study in advancing the field of financial forecasting and the potential benefits of adopting machine learning techniques in predicting stock prices. In conclusion, this study contributes to the growing body of research on the application of machine learning in predicting stock prices. By leveraging the power of advanced algorithms and data analytics, this research aims to provide valuable insights and improve the accuracy of stock price forecasts, ultimately benefiting investors, financial institutions, and the broader financial market.

Thesis Overview