Applications of Machine Learning in Predicting Stock Prices

Thesis Abstract

Abstract
This thesis explores the applications of machine learning techniques in predicting stock prices. The stock market is a complex and dynamic system influenced by multiple factors, making accurate predictions challenging. Machine learning algorithms offer the potential to analyze large datasets and identify patterns that can be used to forecast stock prices. This study focuses on developing and evaluating machine learning models for stock price prediction, with the aim of improving forecasting accuracy and decision-making in financial markets. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The introduction sets the stage for the research by highlighting the importance of stock price prediction and the potential benefits of using machine learning algorithms in this context. Chapter 2 presents a comprehensive literature review that examines existing research on stock price prediction using machine learning techniques. The review covers various approaches, methodologies, and findings from previous studies, providing a foundation for the research methodology in Chapter 3. Chapter 3 details the research methodology employed in this study, including data collection, preprocessing, feature selection, model development, evaluation metrics, and validation techniques. The chapter also discusses the choice of machine learning algorithms and parameter tuning strategies used to build predictive models for stock price forecasting. Chapter 4 presents the discussion of findings derived from evaluating the machine learning models developed in the study. The chapter analyzes the performance of the models, compares results with baseline methods, interprets key findings, and discusses implications for stock price prediction and financial decision-making. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, highlighting contributions to the field, and suggesting avenues for future research. The conclusion also reflects on the limitations of the study and offers recommendations for practitioners and researchers interested in applying machine learning in predicting stock prices. Overall, this thesis contributes to the growing body of research on machine learning applications in finance, specifically in the context of stock price prediction. By leveraging advanced algorithms and techniques, this study aims to enhance the accuracy and efficiency of stock market forecasting, ultimately benefiting investors, financial analysts, and decision-makers in the financial industry.