Thesis Abstract

Abstract
The use of artificial intelligence, specifically neural networks, has gained significant attention in the financial sector due to its potential to accurately predict stock prices. This thesis explores the applications of neural networks in predicting stock prices and analyzes their effectiveness in enhancing investment decision-making. The study begins with an introduction to the topic, providing a background of the study and highlighting the problem statement. The objectives of the study are outlined, along with the limitations and scope of the research. The significance of the study is discussed, emphasizing the potential impact of using neural networks in predicting stock prices. The structure of the thesis is also presented to guide the reader through the research content. Chapter two consists of a comprehensive literature review that examines existing studies on the use of neural networks in predicting stock prices. Ten key items are discussed, including the history of neural networks in finance, different neural network architectures, data preprocessing techniques, and evaluation metrics for predicting stock prices. The literature review provides a foundation for understanding the current state of research in this area and identifies gaps that this study aims to address. Chapter three focuses on the research methodology employed in this study. Eight key contents are discussed, including data collection methods, neural network model selection, feature engineering techniques, training and testing procedures, and performance evaluation metrics. The chapter details the steps taken to develop and train neural network models for predicting stock prices, ensuring transparency and reproducibility of the research process. Chapter four presents an elaborate discussion of the findings obtained from implementing neural networks in predicting stock prices. The results are analyzed in detail, discussing the accuracy of predictions, model performance, and the impact of different factors on the prediction outcomes. The chapter provides insights into the effectiveness of neural networks in stock price prediction and discusses the implications of the findings for investment decision-making in the financial markets. Chapter five concludes the thesis by summarizing the key findings, discussing the contributions of the study to the field of finance, and highlighting avenues for future research. The conclusion emphasizes the importance of using neural networks in predicting stock prices and the potential benefits for investors and financial institutions. Overall, this thesis contributes to the growing body of knowledge on the applications of neural networks in finance and provides valuable insights for researchers, practitioners, and policymakers in the financial industry.

Thesis Overview