Thesis Abstract

Abstract
This thesis explores the applications of neural networks in predicting stock market trends. In recent years, the use of artificial intelligence and machine learning techniques in financial markets has gained significant attention due to their potential to enhance forecasting accuracy and decision-making processes. Neural networks, a subset of artificial intelligence, have shown promising results in predicting stock price movements by analyzing complex patterns and relationships in historical data. The study begins by providing an introduction to the research topic, discussing the background of the study, and presenting the problem statement that motivates the research. The objectives of the study are outlined to investigate the effectiveness of neural networks in predicting stock market trends, while also considering the limitations and scope of the research. The significance of the study is highlighted in terms of its potential impact on financial decision-making processes, risk management, and investment strategies. The structure of the thesis is outlined to guide the reader through the key sections of the research. A comprehensive literature review is conducted to explore existing research on the use of neural networks in predicting stock market trends. The review covers various aspects such as the theoretical foundations of neural networks, different architectures and algorithms used in stock market prediction, and the empirical evidence supporting the effectiveness of neural networks in financial forecasting. The review also discusses the challenges and limitations associated with applying neural networks in stock market prediction. The research methodology section outlines the approach taken to collect and analyze data for the study. Various techniques such as data preprocessing, feature selection, model training, and performance evaluation are discussed in detail. The chapter also includes a description of the dataset used in the study and the evaluation metrics employed to assess the predictive accuracy of the neural network models. The findings chapter presents the results of the empirical analysis, including the performance of neural network models in predicting stock market trends. The discussion focuses on the effectiveness of neural networks compared to traditional statistical models, the impact of different input features on prediction accuracy, and the implications of the findings for financial market participants. The chapter also discusses potential areas for future research and improvements in model performance. In conclusion, the study highlights the potential of neural networks in predicting stock market trends and offers insights into their practical applications in financial markets. The thesis contributes to the growing body of research on artificial intelligence in finance and provides valuable insights for investors, traders, and financial analysts. Overall, the research underscores the importance of leveraging advanced technologies such as neural networks to enhance decision-making processes and improve forecasting accuracy in the dynamic and complex domain of stock market prediction. Keywords Neural Networks, Stock Market Trends, Predictive Modeling, Artificial Intelligence, Financial Forecasting, Machine Learning, Investment Strategies.

Thesis Overview