Thesis Abstract

Abstract
This thesis investigates the effectiveness of various machine learning algorithms in predicting stock prices to assist investors in making informed decisions. Stock price prediction is a critical area of research in financial markets, as accurate forecasting can lead to significant profits or losses. Machine learning techniques have gained popularity in recent years due to their ability to analyze vast amounts of data and identify complex patterns that may be imperceptible to human analysts. The study focuses on comparing the performance of different machine learning algorithms, including Support Vector Machines, Random Forest, Neural Networks, and Gradient Boosting, in predicting stock prices. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance of the study, and the structure of the thesis. The chapter also includes the definition of key terms related to stock price prediction and machine learning algorithms. Chapter Two consists of a comprehensive literature review that examines existing studies on stock price prediction and the application of machine learning algorithms in financial forecasting. The review covers ten key aspects, including the historical context of stock price prediction, the evolution of machine learning in finance, and recent trends in algorithmic trading. Chapter Three outlines the research methodology employed in this study. It details the data collection process, selection of machine learning algorithms, feature engineering techniques, model training and evaluation procedures, and performance metrics used to assess the predictive accuracy of the models. The chapter also discusses the data preprocessing steps and variable selection methods to enhance the predictive power of the models. Chapter Four presents a detailed discussion of the findings obtained from applying different machine learning algorithms to predict stock prices. The chapter evaluates the performance of each algorithm based on metrics such as accuracy, precision, recall, and F1 score. The results are analyzed and compared to identify the strengths and weaknesses of each algorithm in predicting stock prices accurately. Chapter Five concludes the thesis by summarizing the key findings, implications of the study, and recommendations for future research in the field of stock price prediction using machine learning algorithms. The conclusion highlights the significance of the research in improving investment decision-making and provides insights into the potential applications of machine learning in financial markets. In conclusion, this thesis contributes to the growing body of knowledge on the application of machine learning algorithms in predicting stock prices. By comparing the performance of various algorithms, this study offers valuable insights for investors, financial analysts, and researchers seeking to leverage machine learning techniques for more accurate and reliable stock price forecasts.

Thesis Overview