Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms for predicting stock market trends. The stock market is a complex and dynamic system influenced by various factors, making accurate prediction a challenging task. Machine learning techniques have shown promise in analyzing large datasets and identifying patterns that can be used to forecast future market movements. This research aims to investigate the effectiveness of different machine learning algorithms in predicting stock prices and trends. The study begins with a comprehensive introduction that outlines the background of the research, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The introduction also includes a definition of key terms used throughout the thesis to provide clarity and understanding of the concepts discussed. Chapter two presents a detailed literature review that examines existing research on machine learning applications in stock market prediction. The review covers various machine learning algorithms, methodologies, and approaches used in previous studies, highlighting their strengths, weaknesses, and areas for improvement. This chapter provides a solid foundation for the research methodology and helps in identifying gaps in the existing literature. Chapter three focuses on the research methodology employed in this study. It includes discussions on data collection methods, dataset preprocessing, feature selection, model training, evaluation metrics, and validation techniques. The chapter also outlines the experimental setup and describes how different machine learning algorithms are implemented and compared for predicting stock market trends. Chapter four presents an elaborate discussion of the findings obtained from the experiments conducted in the study. The results of applying various machine learning algorithms to predict stock prices and trends are analyzed, compared, and interpreted. The chapter discusses the performance of each algorithm, the accuracy of predictions, and the factors influencing the results. Chapter five concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future work. The conclusion highlights the contributions of the study to the field of stock market prediction using machine learning algorithms and discusses potential areas for further research and improvement. In conclusion, this thesis contributes to the ongoing research in applying machine learning algorithms for predicting stock market trends. By exploring different algorithms, methodologies, and approaches, the study provides valuable insights into the effectiveness of machine learning techniques in forecasting stock prices. The findings of this research can potentially benefit investors, financial analysts, and researchers looking to leverage machine learning for more accurate stock market predictions.

Thesis Overview