Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms for predicting stock prices. The financial markets are characterized by volatility and complexity, making accurate predictions of stock prices a challenging task. Traditional methods have limitations in capturing the intricate patterns and trends present in stock price data. Machine learning, with its ability to learn from data and make predictions, offers a promising approach to address this challenge. The study begins with an introduction to the background of using machine learning in financial markets and the problem statement of predicting stock prices accurately. The objectives of the study are to evaluate the effectiveness of machine learning algorithms in predicting stock prices and to compare their performance with traditional methods. The limitations and scope of the study are also discussed, along with the significance of applying machine learning in stock price prediction. A thorough literature review is conducted in Chapter Two, which covers ten key areas related to machine learning algorithms, stock market prediction, and previous research in the field. This review provides a comprehensive understanding of the existing knowledge and gaps in the literature. Chapter Three details the research methodology employed in this study, including data collection, preprocessing, feature selection, model training, and evaluation techniques. The chapter outlines the steps taken to implement various machine learning algorithms and compares their performance based on metrics such as accuracy, precision, and recall. Chapter Four presents the findings of the study, including the comparative analysis of different machine learning algorithms in predicting stock prices. The results highlight the strengths and weaknesses of each algorithm and provide insights into their practical applications in the financial markets. The chapter also discusses the implications of the findings and potential areas for future research. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications for the financial industry, and suggesting recommendations for further research. The study contributes to the growing body of knowledge on using machine learning for stock price prediction and offers valuable insights for investors, financial analysts, and researchers in the field. In conclusion, this thesis demonstrates the potential of machine learning algorithms in predicting stock prices and provides a comparative analysis of their performance. By leveraging the power of data-driven approaches, this study offers new perspectives on forecasting stock prices and opens up avenues for further research and practical applications in the financial markets.

Thesis Overview