Thesis Abstract

Abstract
The stock market is a complex and dynamic system influenced by numerous factors, making accurate prediction of market trends a challenging task. In recent years, machine learning techniques have gained significant attention for their potential to improve stock market predictions. This thesis explores the application of machine learning in predicting stock market trends with the aim of enhancing investment decision-making processes. The study begins with a comprehensive literature review to investigate existing methodologies and approaches in the field. Various machine learning algorithms are analyzed to determine their effectiveness in predicting stock market trends. The research methodology section outlines the data collection process, feature selection techniques, model training, and evaluation methods employed in the study. The findings from the analysis of historical stock market data using machine learning models are discussed in detail, highlighting the performance and accuracy of the predictive models. The implications of the results are examined in the context of investment strategies and risk management. The study concludes with a summary of key findings, implications for future research, and recommendations for investors and financial institutions looking to leverage machine learning for stock market predictions. This thesis contributes to the growing body of knowledge on the application of machine learning in finance and provides valuable insights for improving decision-making processes in the stock market.

Thesis Overview