Thesis Abstract

Abstract
The stock market is a complex and dynamic system influenced by various factors, making accurate predictions challenging yet crucial for investors and financial analysts. This thesis explores the application of machine learning techniques in predicting stock market trends. The study aims to leverage the power of artificial intelligence to develop models that can analyze historical data, identify patterns, and forecast future stock prices with improved accuracy. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of utilizing machine learning in stock market prediction. Chapter 2 conducts a comprehensive literature review covering ten key areas related to stock market prediction, machine learning algorithms, financial data analysis, and previous studies in the field. The review synthesizes existing knowledge to identify gaps and opportunities for further research in the domain of applying machine learning to predict stock market trends. Chapter 3 outlines the research methodology employed in this study, detailing the data collection process, preprocessing techniques, feature selection, model development, and evaluation metrics. The chapter also discusses the ethical considerations and potential biases that may impact the research outcomes. Chapter 4 presents an in-depth analysis of the findings obtained from implementing machine learning models on historical stock market data. The discussion highlights the performance of different algorithms, model accuracy, predictive capabilities, and potential challenges encountered during the research process. Chapter 5 concludes the thesis by summarizing the key findings, implications of the study, contributions to the existing literature, and recommendations for future research directions. The conclusion reflects on the effectiveness of machine learning in predicting stock market trends and its practical applications for investors and financial institutions. Overall, this thesis contributes to the growing body of research on utilizing machine learning in the financial sector, particularly in forecasting stock market trends. By leveraging advanced algorithms and data analytics techniques, this study aims to enhance the accuracy and reliability of stock price predictions, ultimately benefiting stakeholders in making informed investment decisions in a volatile market environment.

Thesis Overview