Thesis Abstract

Abstract
This thesis explores the applications of machine learning techniques in predicting stock market trends. The stock market is a complex and dynamic system influenced by various factors such as economic indicators, geopolitical events, market sentiment, and investor behavior. Traditional methods of stock market analysis often struggle to capture the intricacies of this system effectively. Machine learning, a subset of artificial intelligence, has gained popularity in recent years for its ability to analyze large datasets, identify patterns, and make predictions based on historical data. The primary objective of this study is to investigate the effectiveness of machine learning algorithms in predicting stock market trends and to compare their performance with traditional statistical methods. The research methodology involves collecting historical stock market data, preprocessing the data, selecting appropriate features, training and testing machine learning models, and evaluating their predictive accuracy. Chapter 1 provides an introduction to the research topic, background information on the stock market, a problem statement highlighting the limitations of traditional methods, objectives of the study, the scope and significance of the research, and a definition of key terms. Chapter 2 presents a comprehensive literature review covering various machine learning algorithms used in stock market prediction, previous studies in the field, and comparisons between machine learning and traditional methods. Chapter 3 details the research methodology, including data collection, preprocessing techniques, feature selection, model selection, training and testing procedures, evaluation metrics, and validation methods. The chapter also discusses the ethical considerations and potential biases in the dataset. Chapter 4 presents the findings of the study, including the performance of different machine learning algorithms in predicting stock market trends, comparison with traditional methods, feature importance analysis, and insights gained from the analysis of the results. The chapter also discusses the limitations of the study and potential areas for future research. Chapter 5 concludes the thesis by summarizing the key findings, highlighting the contributions of the study to the field of stock market prediction, discussing practical implications for investors and financial institutions, and suggesting avenues for further research. Overall, this thesis contributes to the growing body of research on the application of machine learning in predicting stock market trends and provides valuable insights into the potential benefits and challenges of using advanced computational techniques in financial markets.

Thesis Overview