Thesis Abstract

Abstract
This thesis explores the applications of machine learning in predicting stock prices, a critical area of study in the financial market. The primary objective of this research is to investigate the effectiveness of machine learning algorithms in forecasting stock prices and to assess their potential impact on investment decision-making. The study is motivated by the increasing complexity and volatility of financial markets, which necessitate more advanced tools and techniques for accurate predictions. The research begins with a comprehensive review of the existing literature on machine learning models and their applications in financial forecasting. This literature review highlights the significance of machine learning algorithms in analyzing historical stock data, identifying patterns and trends, and making predictions based on these patterns. The review also discusses the limitations and challenges associated with traditional forecasting methods and the potential advantages offered by machine learning approaches. The methodology chapter outlines the research design, data collection methods, and the selection of machine learning algorithms for the study. The research methodology incorporates both quantitative and qualitative analysis techniques to evaluate the performance of different machine learning models in predicting stock prices. The chapter also discusses the criteria for selecting the dataset, preprocessing steps, feature engineering, and model evaluation metrics. The findings chapter presents a detailed analysis of the experimental results obtained from applying various machine learning algorithms to real-world stock price data. The chapter evaluates the accuracy, precision, recall, and other performance metrics of the models and compares their predictive capabilities. The discussion of findings includes a critical assessment of the strengths and limitations of each algorithm and provides insights into their practical implications for stock market forecasting. The conclusion chapter summarizes the key findings of the study and draws conclusions on the effectiveness of machine learning in predicting stock prices. The research highlights the potential benefits of using machine learning algorithms for stock market analysis and decision-making and proposes recommendations for future research in this area. The study contributes to the growing body of knowledge on the application of machine learning in finance and offers valuable insights for investors, financial analysts, and researchers interested in leveraging advanced technologies for stock price predictions. Overall, this thesis provides a comprehensive analysis of the applications of machine learning in predicting stock prices and offers valuable insights into the potential of these technologies to enhance decision-making processes in the financial market. The research contributes to the ongoing debate on the use of machine learning in finance and highlights the need for further exploration and refinement of these techniques for more accurate and reliable stock price predictions. Word Count 307

Thesis Overview