Application of Neural Networks in Predicting Stock Market Trends

Thesis Abstract

Abstract
The rapid advancements in technology have paved the way for the application of neural networks in various fields, including finance. This thesis explores the feasibility and effectiveness of using neural networks to predict stock market trends. The study aims to address the growing need for accurate and timely predictions in the financial market, which can significantly impact investment decisions. The research begins with an introduction, providing a background of the study and highlighting the problem statement. The objectives of the study include evaluating the performance of neural networks in predicting stock market trends and identifying any limitations that may arise during the process. The scope of the study focuses on analyzing historical stock market data and applying neural network models to generate predictions. The significance of the study lies in its potential to enhance decision-making processes in financial markets. Chapter two presents a comprehensive literature review, encompassing ten key areas related to neural networks, stock market trends, and previous research studies in the field. This section aims to build a solid theoretical foundation for the research and identify gaps that warrant further investigation. Chapter three outlines the research methodology, detailing the approach taken to collect and analyze data, select neural network models, and evaluate their performance. The chapter includes subsections on data collection, preprocessing, model selection, training, and testing procedures, as well as performance evaluation metrics. In chapter four, the discussion of findings delves into the results obtained from applying neural networks to predict stock market trends. The analysis includes the comparison of predicted trends with actual market movements, evaluating the accuracy and reliability of the neural network models. This section also discusses any challenges faced during the implementation process and offers insights into potential improvements for future research. Finally, chapter five presents the conclusion and summary of the thesis, highlighting key findings, implications, and recommendations for further research. The study concludes that neural networks show promise in predicting stock market trends, but further refinement and optimization are necessary to enhance their predictive capabilities. Overall, this research contributes to the growing body of knowledge on the application of neural networks in finance and provides valuable insights for investors, financial analysts, and researchers in the field.