Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms in predicting stock market trends. The research focuses on developing predictive models that leverage historical stock market data to forecast future trends. In recent years, machine learning has gained significant traction in the financial sector due to its ability to analyze vast amounts of data and identify patterns that traditional statistical methods may overlook. This study aims to contribute to the growing body of research on using machine learning in financial forecasting by specifically focusing on stock market trends. The research begins with a comprehensive literature review in Chapter Two, which examines existing studies on machine learning applications in finance and stock market prediction. The review highlights the various algorithms and techniques commonly used in this area, providing a foundation for the research methodology in Chapter Three. Chapter Three details the research methodology, including data collection, preprocessing, feature selection, model training, and evaluation. The chapter also discusses the selection of machine learning algorithms and parameters for the predictive models. In Chapter Four, the findings of the study are presented and discussed in detail. The performance of the developed predictive models is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The chapter also analyzes the impact of different factors on the predictive accuracy of the models, such as the choice of features, data preprocessing techniques, and algorithm selection. The conclusion and summary in Chapter Five provide a comprehensive overview of the research findings and their implications for stock market prediction using machine learning algorithms. The study concludes with recommendations for further research and practical applications in the financial industry. Overall, this thesis contributes to the field of financial forecasting by demonstrating the effectiveness of machine learning algorithms in predicting stock market trends. By leveraging historical data and advanced analytics techniques, financial professionals can make more informed decisions and better manage risk in the dynamic stock market environment.

Thesis Overview